624 results
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2. 强韧竹木混合未漂长纤浆生产实践.
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刘平原, 原兆兵, 周鲲鹏, 徐应盛, 邱鹏福, and 周嘉玮
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WOOD chips ,POTASSIUM ions ,CHLORIDE ions ,PAPER pulp ,BATCH processing ,BLEACHING (Chemistry) ,PAPER industry - Abstract
Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2022
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3. Adaptive soft sensor using stacking approximate kernel based BLS for batch processes.
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Zhao, Jinlong, Yang, Mingyi, Xu, Zhigang, Wang, Junyi, Yang, Xiao, and Wu, Xinguang
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BATCH processing ,MANUFACTURING processes ,MACHINE learning ,ITERATIVE learning control ,DATABASES ,DETECTORS ,RESISTANCE training - Abstract
To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms. [ABSTRACT FROM AUTHOR]
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- 2024
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4. Flexible job-shop scheduling problem with parallel batch machines based on an enhanced multi-population genetic algorithm.
- Author
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Xue, Lirui, Zhao, Shinan, Mahmoudi, Amin, and Feylizadeh, Mohammad Reza
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FLOW shops ,PRODUCTION scheduling ,BATCH processing ,GENETIC algorithms ,TABU search algorithm ,PARALLEL processing ,COMBINATORIAL optimization ,MACHINERY - Abstract
The flexible job-shop scheduling problem (FJSP) with parallel batch processing machine (PBM) is one of those long-standing issues that needs cutting-edge approaches. It is a recent extension of standard flexible job shop scheduling problems. Despite their wide application and prevalence in practical production, it seems that current research on these types of combinatorial optimization problems remains limited and uninvestigated. More specifically, existing research mainly concentrates on the flow shop scenarios in parallel batch machines for job shop scheduling but few literature emphasis on the flexible job shop integration in these contexts. To directly address the above mentioned problems, this paper establishes an optimization model considering parallel batch processing machines, aiming to minimize the maximum completion time in operating and production environments. The proposed solution merges variable neighborhood search with multi-population genetic algorithms, conducting a neighborhood search on the elite population to reduce the likelihood of falling into local optima. Subsequently, its applicability was evaluated in computational experiments using real production scenarios from a partnering enterprise and extended datasets. The findings from the analyses indicate that the enhanced algorithm can decrease the objective value by as much as 15% compared to other standard algorithms. Importantly, the proposed approach effectively resolves flexible job shop scheduling problems involving parallel batch processing machines. The contribution of the research is providing substantial theoretical support for enterprise production scheduling. [ABSTRACT FROM AUTHOR]
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- 2024
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5. PERFORMANCE COMPARISON OF APACHE SPARK AND HADOOP FOR MACHINE LEARNING BASED ITERATIVE GBTR ON HIGGS AND COVID-19 DATASETS.
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SEWAL, PIYUSH and SINGH, HARI
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DISTRIBUTED computing ,GRAPH algorithms ,BATCH processing ,REGRESSION trees ,COMPUTING platforms ,SQL ,MACHINE learning - Abstract
In the realm of distributed computing frameworks, such as Apache Spark and MapReduce Hadoop, the efficacy of these frameworks varies across diverse applications and algorithms contingent upon distinctive evaluation metrics and critical parameters. This research paper diligently scrutinizes the extant body of research that compares these two frameworks concerning said evaluation metrics and parameters. Subsequently, it conducts empirical investigations to authenticate the performance of these frameworks in the context of an iterative Gradient Boosting Tree Regression (GBTR) algorithm. Remarkably, the comparative analyses in previous studies encompass a spectrum of iterative machine learning regression and classification techniques, batch processing, SQL, and Graph processing algorithms. Furthermore, numerous investigations have explored the application of machine learning algorithms encompassing logistic regression, Page Rank, K-Means, KNN, and the HiBench suite. This paper presents the comparison between the two distributed computing platforms on iterative GBTR for classification task on the HIGGS dataset from the physics domain and for the regression task on the Covid-19 dataset from the healthcare domain. The empirical findings corroborate that Apache Spark exhibits superior execution speed in iterative tasks when the available physical memory significantly exceeds the dataset size. Conversely, Hadoop outperforms Spark when dealing with substantial datasets or constrained physical memory resources. [ABSTRACT FROM AUTHOR]
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- 2024
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6. Time-Specific Thresholds for Batch Process Monitoring: A Study Based on Two-Dimensional Conditional Variational Auto-Encoder.
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Zhu, Jinlin, Liu, Zhong, Lou, Xuyang, Gao, Furong, and Zhang, Zheng
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BATCH processing ,STATISTICAL process control - Abstract
This paper studies the use of varying threshold in the statistical process control (SPC) of batch processes. The motivation is driven by how when multiple phases are implicated in each repetition, the distributions of the features behind vary with phases or even the time; thus, it is inconsistent to uniformly bound them by an invariant threshold. In this paper, we paved a new path for learning and monitoring batch processes based on an efficient framework integrating a model termed conditional dynamic variational auto-encoder (CDVAE). Phase indicators are first used to split the data and are then separated, serving as an extra input for the model in order to alleviate the learning complexity. Dissimilar to the routine using features across all timescales, only features relevant to local timestamps are aggregated for threshold calculation, producing a varying threshold that is more specific for the process variations occurring among the timeline. Leveraged upon this idea, a fault detection panel is devised, and a deep reconstruction-based contribution diagram is illustrated for locating the faulty variables. Finally, the comparative results from two case studies highlight the superiority in both detection accuracy and diagnostic performance. [ABSTRACT FROM AUTHOR]
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- 2024
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7. A batch process for high dimensional imputation.
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Waggoner, Philip D.
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BATCH processing ,RANDOM forest algorithms ,MISSING data (Statistics) ,OPEN scholarship ,ELECTRONIC data processing - Abstract
This paper describes a correlation-based batch process for addressing high dimensional imputation problems. There are relatively few algorithms designed to efficiently handle imputation of missing data in high dimensional contexts. Fewer still are flexible enough to natively handle mixed-type data, often requiring lengthy pre-processing to get the data into proper shape, and then post-processing to return the data to usable form. Such decisions as well as assumptions made by many methods (e.g., data generating process) limit their performance, flexibility, and usability. Built on a set of complementary algorithms for nonparametric imputation via chained random forests, I introduce a batching process to ease computational costs associated with high dimensional imputation by subsetting data based on ranked cross-feature absolute correlations. The algorithm then imputes each batch separately, and joins imputed subsets in the final step. The process, hdImpute, is fast and accurate. As a result, high dimensional imputation is more accessible, and researchers are not forced to decide between speed or accuracy. Complementary software is available in the form of an R package, and is openly developed on Github under the MIT public license. In the spirit of open science, collaboration and engagement with the actively developing software are encouraged. [ABSTRACT FROM AUTHOR]
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- 2024
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8. Joint production-planning and distribution optimization of perishable products under a combined shipment structure: A new hybrid policy-based approach.
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Rezaei, S. and Kheirkhah, A.
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PRODUCTION planning ,SUPPLY chains ,BATCH processing ,HEURISTIC ,CONSUMERS - Abstract
Following the pioneering challenge for joint optimization of decisions in supply chains, this paper newly provides an integrated framework to efficiently fulfill a productionplanning and distribution problem in effect. In such an integrated scheme, a set of perishable family products is manufactured on a single batch-processing machine. These products are dispatched to the customers by a third-party logistics service provider with only two types of licensed eco-friendly transportation facilities. In order to efficiently deliver the manufactured products before they become unusable, we propose a combined shipment structure. To accomplish this, the problem is formulated in the context of a mixedinteger linear programming model. In particular, we aim to establish two manufacturing policies based on both increasing and decreasing rates of production and also two delivery policies expressing distinct preferences in fulfilling the customers' demands. The cost structures obtained from the established integrated planning and the resulting distribution configurations are investigated as well. Further, four heuristic algorithms are developed for solving the problem for each hybrid production/distribution scheme derived from the former policies. A numerical study is conducted to compare the mentioned procedures, illustrating the preferable efficiency of the plan obtained through the hybridization of the increasing-production-rate and decreasing-delivery-distance policies. [ABSTRACT FROM AUTHOR]
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- 2024
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9. Bio-Based Disposable Containers for Food Services.
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Barretto, Roselle, Qi, Guangyan, Jones, Christopher, Li, Yonghui, Sun, Xiuzhi Susan, and Wang, Donghai
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FOOD containers ,NATURAL fibers ,FOOD service ,AERODYNAMIC heating ,RAW materials ,BATCH processing ,BIODEGRADABLE plastics - Abstract
Currently, petrochemical plastics dominate the food service industry due to their good mechanical properties and barrier against heat, water vapor, carbon dioxide, and oxygen. This widespread use is not only harmful to humans but also to the ecosystem as synthetic plastics disrupt ecological balance and deplete petroleum-based oil resources. Researchers and manufacturers are continuously addressing this problem by developing bio-based alternatives that provide numerous advantages including structural flexibility, biodegradability, and effective barrier properties. However, the high cost of production and unavailability of equipment for batch processing impede the potential for widespread manufacturing. Natural fibers mixed with bio-based adhesives derived from plants provide one of the biggest potential sources of bio-based materials for the food container industry. Not only does this address the issue of high raw material cost but it also has the potential to become sustainable once processing steps have been optimized. In this review, the current findings of several research related to the production of bio-based disposable food containers, packaging, and composites made from bio-based materials and bio-based adhesives are critically discussed. Several properties and characteristics important to the production of food service containers and primary packaging, as well as the existing challenges and future perspectives, are also highlighted. [ABSTRACT FROM AUTHOR]
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- 2024
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10. recolorize: An R package for flexible colour segmentation of biological images.
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Weller, Hannah I., Hiller, Anna E., Lord, Nathan P., and Van Belleghem, Steven M.
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FLEXIBLE packaging , *IMAGE segmentation , *COLOR , *BATCH processing , *BIOLOGICAL variation - Abstract
Colour pattern variation provides biological information in fields ranging from disease ecology to speciation dynamics. Comparing colour pattern geometries across images requires colour segmentation, where pixels in an image are assigned to one of a set of colour classes shared by all images. Manual methods for colour segmentation are slow and subjective, while automated methods can struggle with high technical variation in aggregate image sets. We present recolorize, an R package toolbox for human‐subjective colour segmentation with functions for batch‐processing low‐variation image sets and additional tools for handling images from diverse (high‐variation) sources. The package also includes export options for a variety of formats and colour analysis packages. This paper illustrates recolorize for three example datasets, including high variation, batch processing and combining with reflectance spectra, and demonstrates the downstream use of methods that rely on this output. [ABSTRACT FROM AUTHOR]
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- 2024
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11. Learning Priority Indices for Energy-Aware Scheduling of Jobs on Batch Processing Machines.
- Author
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Schorn, Daniel Sascha and Moench, Lars
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BATCH processing ,PRODUCTION scheduling ,SEMICONDUCTOR wafers ,SCHEDULING ,GENETIC programming - Abstract
A scheduling problem for parallel batch processing machines (BPMs) with jobs having unequal ready times in semiconductor wafer fabrication facilities (wafer fabs) is studied in this paper. A blended objective function combining the total weighted tardiness (TWT) and the total electricity cost (TEC) under a time-of-use (TOU) tariff is considered. A genetic programming (GP) procedure is designed to automatically discover priority indices for a heuristic scheduling framework. Results of computational experiments are reported that demonstrate that the learned priority indices lead to high-quality schedules in a short amount of computing time. [ABSTRACT FROM AUTHOR]
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- 2024
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12. Batch production prediction for the mechanical cutting industry based on process capability.
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Xu, Guangtao, Liu, Tianyi, Wang, Weichuan, Qiao, Zeyuan, Wang, Gang, Peng, Zhenlong, and Zhao, Minghao
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PROCESS capability ,PRODUCT quality ,BATCH processing ,FORECASTING ,DIAMETER - Abstract
In the mechanical cutting industry, trial production is used for predicting and evaluating the quality of product processes before batch production, and it can be expressed through the qualification rate. However, it cannot objectively and comprehensively evaluate the quality of product processes. This study optimizes the analysis of outliers and stability in mathematical statistics to better apply it in the mechanical cutting industry; then, it combines them with process capability analysis. Simultaneously, considering the non-normal distribution of process parameters, a batch production-prediction model is proposed. The reliability of batch production-prediction model is verified by the diameter, roundness and roughness of structural common samples. Meanwhile, for other mechanical parts in the mechanical cutting industry, the model proposed in this paper can be used to quickly and accurately predict and evaluate batch production. [ABSTRACT FROM AUTHOR]
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- 2024
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13. A Simple and Efficient Approach to Determine Lithium and Multi-Elements in Lithium-Bearing Clay Minerals through a Partial Extraction Using Ammonium Hydrogen Fluoride.
- Author
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Zhang, Feige, Zhang, Qinfeng, Chen, Zhigui, Fang, Xiaoqing, Yu, Xiaoqi, Li, Guangyi, and Chen, Di
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HYDROGEN fluoride ,CLEAN energy ,CLAY minerals ,AMMONIUM fluoride ,BATCH processing ,TRACE elements - Abstract
Lithium (Li) is vital to the world economy as an irreplaceable element in new-generation sustainable energy applications. Quantifying Li levels in clay minerals, a key new resource, supports extensive mineralization surveys. The major limitation in these analyses is complete decomposition, which often requires the use of strong acids and is labor-intensive, particularly in batch processing. This paper presents a partial extraction strategy for quantifying Li and multiple elements, employing ammonium hydrogen fluoride to extract from clay minerals while avoiding the use of various strong acids. Following extraction, the efficiency for Li reaches 98.61%, with values ranging from 62.68% to 91.43% for multiple elements including three major elements (Ca, Fe, Mg) and eleven trace elements (Be, Co, Cr, Cu, Ga, Mn, Ni, P, Rb, Sr, V), demonstrating favorable precision (RSD below 7.34%). This approach offers a promising tool for a simple and efficient batch analysis in the survey of mineralization in Li-bearing clay deposits. [ABSTRACT FROM AUTHOR]
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- 2024
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14. Fast Algorithm of Passive Bistatic Radar Detection Based on Batches Processing of Sparse Representation and Recovery.
- Author
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Cui, Kai, Wang, Changlong, Zhou, Feng, Liu, Chunheng, Gao, Yongchan, and Feng, Weike
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BISTATIC radar ,BATCH processing ,PASSIVE radar ,ALGORITHMS ,PARALLEL algorithms ,COMPUTATIONAL complexity - Abstract
In the passive bistatic radar (PBR) system, methods exist to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delay-Doppler domain of the cross ambiguity function (CAF) to detect weak targets. However, the modeling and solving of this method involve substantial memory consumption and computational complexity. To address these challenges, this paper establishes a target detection model for PBR based on batch processing of sparse representation and recovery. This model partitions the CAF into blocks, identifies blocks requiring processing based on the presence of targets, and improves the construction and utilization of the measurement matrix. This results in a reduction in the computational complexity and memory resource requirements for sparse representation and recovery, and provides favorable conditions for parallel execution of the algorithm. Experimental results indicate that the proposed approach increases the number of blocks by a factor of four, and reduces the number of real multiplications by approximately an order of magnitude. Hence, compared with the traditional approach, the proposed approach enables fast and stable detection of weak targets. [ABSTRACT FROM AUTHOR]
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- 2024
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15. Preparation of Activated Carbon Derived from Waste Peanut Shells to Remove Dye from Water in a Batch System
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Zheng Liu and Xiaohuan Zhang
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Materials science ,Contamination ,Condensed Matter Physics ,Pulp and paper industry ,Electronic, Optical and Magnetic Materials ,Incineration ,Control and Systems Engineering ,Materials Chemistry ,Ceramics and Composites ,Batch processing ,medicine ,Electrical and Electronic Engineering ,Activated carbon ,medicine.drug - Abstract
Majority of peanut shells are thrown away or incinerated, which give rise to wasting resources and potential contamination. In this study, activated carbon (AC) from waste peanut shells were prepar...
- Published
- 2021
16. The CloudVeneto initiative: 10 years of operations to support interdisciplinary open science.
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Andreetto, Paolo, Costa, Fulvia, Crescente, Alberto, Fantinel, Sergio, Fanzago, Federica, Garfagnini, Alberto, Lazzaro, Loris, Lenzo, Rita, Mazzon, Paolo Emilio, Menguzzato, Matteo, Sella, Gianpietro, Sgaravatto, Massimo, Traldi, Sergio, Verlato, Marco, and Zangrando, Lisa
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CLOUD computing ,PARTICLE physics ,SCIENTIFIC community ,COMPACT muon solenoid experiment ,BATCH processing - Abstract
CloudVeneto is a private cloud targeted to scientific communities based on OpenStack software. It was designed in 2013 and put in operation one year later, to support INFN projects, mainly HEP ones. Its resources are physically distributed among two sites: the Physics Department of University of Padova-INFN Padova Unit and the INFN Legnaro National Laboratories. During these 10 years CloudVeneto evolved to integrate also resources funded by ten Departments of the University of Padova, and to support several scientific disciplines of different domains. The use cases the communities have to face up often show a common pattern. This was an opportunity for us to develop and improve the services on our infrastructure to provide common solutions to different use cases. It happened for example with the Container as a Service (CaaS) that makes the management of Kubernetes clusters easier from a user point of view. Moreover, CloudVeneto joined the INFN national cloud infrastructure (INFN Cloud), making available some resources to this federated infrastructure. CloudVeneto is also involved in an R&D project to realize a distributed analysis facility for the CMS experiment based on the HTCondor batch system. In this paper we describe some use-cases of different projects pointing out the common patterns and the new implementations and configurations done in the infrastructure. [ABSTRACT FROM AUTHOR]
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- 2024
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17. Effects of Pharmaceuticals on the Performance of Earthen Pot Microbial Fuel Cell
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Manaswini Behera and Somdipta Bagchi
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Environmental Engineering ,Microbial fuel cell ,General Chemical Engineering ,Geotechnical Engineering and Engineering Geology ,Pulp and paper industry ,Trimethoprim ,chemistry.chemical_compound ,chemistry ,Batch processing ,medicine ,Environmental Chemistry ,Degradation (geology) ,Waste Management and Disposal ,Salicylic acid ,Water Science and Technology ,medicine.drug - Abstract
A study was conducted to introduce an efficient alternative such as bioelectrochemical degradation of pharmaceuticals. Six earthen pot MFCs were operated in batch mode, in which the pharma...
- Published
- 2022
18. Challenges of Big Data and Vehicle Data
- Author
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Christian Prehofer
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Big data processing ,Stream processing ,Cloud processing ,business.industry ,Computer science ,Big data ,Short paper ,Real-time computing ,Batch processing ,Use case ,business ,Edge computing - Abstract
In this short paper, we outline the challenges of Big Data processing for vehicle data, considering different use cases as well as different locations. On an architectural level, we show the benefits and possible limitations of the different approaches for in-vehicle processing, edge computing as well as cloud processing. We also discuss different approaches for Big Data Processing, including batch processing as well as distributed stream processing.
- Published
- 2021
19. Continuous flow electrocoagulation process in combination with other treatment processes: A review of current applications and approaches.
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Hameed, Y. T., Hameed, M. A. R., and Aday, L. A.
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BIOLOGICAL nutrient removal ,WASTEWATER treatment ,BATCH processing - Abstract
The combination of electrocoagulation (EC) with other treatment techniques is a recent approach to achieve better removal of pollutants. Numerous reported experiments revealed the feasibility of combining EC with various physical, chemical, and biological treatment processes. However, most of the experiments reported the behavior of EC units in batch process, other experiments were more realistic as they were conducted in a continuous flow process. There have been numerous review papers highlighted different points regarding EC, these review papers covered the removal of a specific pollutant, treatment of specific wastewater, operational factors, type of the treatment process that is integrated with EC, or applications of advanced EC. Nevertheless, the type of EC process, batch or continuous, was overlooked in these reviews. This manuscript reviewed the combination of continuous flow EC process with other treatment techniques. The outcomes of this study showed the efficient performance of these combinations to remove various pollutants, however, a potential deterioration of removal efficiency for a certain pollutant might occur especially those pollutants which are removed by post biological nutrients removal. Moreover, real wastewater was used in most of the reviewed studies confirming the applicability of the results and conclusions, on the other hand, the majority of the studies were conducted using bench scale units which may lessen the authenticity of the results. Furthermore, a lack of comprehensive costing studies and kinetic studies of these combinations is noticeable. Finally, the majority of the reviewed papers investigated EC as a pretreatment process, accordingly an obvious gap of EC as a post treatment process should be considered. [ABSTRACT FROM AUTHOR]
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- 2022
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20. Polygon Simplification for the Efficient Approximate Analytics of Georeferenced Big Data.
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Al Jawarneh, Isam Mashhour, Foschini, Luca, and Bellavista, Paolo
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BIG data ,BATCH processing ,SENSOR networks ,URBAN planning ,DECISION making - Abstract
The unprecedented availability of sensor networks and GPS-enabled devices has caused the accumulation of voluminous georeferenced data streams. These data streams offer an opportunity to derive valuable insights and facilitate decision making for urban planning. However, processing and managing such data is challenging, given the size and multidimensionality of these data. Therefore, there is a growing interest in spatial approximate query processing depending on stratified-like sampling methods. However, in these solutions, as the number of strata increases, response time grows, thus counteracting the benefits of sampling. In this paper, we originally show the design and realization of a novel online geospatial approximate processing solution called GeoRAP. GeoRAP employs a front-stage filter based on the Ramer–Douglas–Peucker line simplification algorithm to reduce the size of study area coverage; thereafter, it employs a spatial stratified-like sampling method that minimizes the number of strata, thus increasing throughput and minimizing response time, while keeping the accuracy loss in check. Our method is applicable for various online and batch geospatial processing workloads, including complex geo-statistics, aggregation queries, and the generation of region-based aggregate geo-maps such as choropleth maps and heatmaps. We have extensively tested the performance of our prototyped solution with real-world big spatial data, and this paper shows that GeoRAP can outperform state-of-the-art baselines by an order of magnitude in terms of throughput while statistically obtaining results with good accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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21. Effect Of Hydrodynamic Conditions On The Formation And Structure Of Aerobic Granular Sludge Performing Enhanced Biological Phosphorus Removal
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Yan Zhou, Gulsum Yilmaz, Ender Cetin, and Karin Aleksanyan Magden
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Environmental Engineering ,Enhanced biological phosphorus removal ,Chemistry ,Settling time ,Batch processing ,Sewage treatment ,Management, Monitoring, Policy and Law ,Pulp and paper industry ,Pollution ,Water Science and Technology - Abstract
The study investigated the effect of hydrodynamic conditions on the formation of enhanced biological phosphorus removal (EBPR) granular sludge without losing the EBPR capacity, and its structure. The effect of hydrodynamic conditions on the formation of EBPR granular sludge was evaluated by using the minimum settling velocity, (V-s)(min), and superficial air velocity. The application of (V-s)(min) strategy was proved to be beneficial in maintaining granular biomass without losing the EBPR capacity. Microscale structure, the chemical composition and chemically stored phosphorus of the matured EBPR granular sludge were determined by using ESEM-EDX and cold perchloric acid (PCA) analyses. The results of both the ESEM-EDX and PCA analyses proved that the poly-P granules were stored extracellular in EPS structure of EBPR granular sludge. Therefore, the EBPR granular sludge has a high potential for both removal and recovery of phosphorus from wastewater, which needs to be investigated in future studies.
- Published
- 2022
22. Two-agent scheduling on mixed batch machines to minimise the total weighted makespan.
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Fan, Guo-Qiang, Wang, Jun-Qiang, and Liu, Zhixin
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BATCH processing ,PRODUCTION scheduling ,APPROXIMATION algorithms ,SCHEDULING ,MACHINERY - Abstract
This paper studies a two-agent scheduling problem on mixed batch machines in parallel. A mixed batch machine can process several jobs simultaneously as a batch, as long as the number of jobs in the batch does not exceed the machine capacity. The processing time of a mixed batch is the weighted sum of the maximum processing time and the total processing time of jobs in the batch. The objective is to minimise the weighted sum of two agents' makespans. We present four approximation algorithms based on two strategies: the machine-centric strategy and the agent-centric strategy. For each strategy, a full batch longest processing time (FBLPT) rule and a longest processing time greedy (LPTG) rule are used. We conduct theoretical analyses based on the worst-case performance ratio to provide the provable guarantees on the performances of the algorithms, and simulation analyses based on randomly generated instances to evaluate the average performances of the algorithms. Furthermore, we verify the consistency between the theoretical and simulation results. The algorithms using agent-centric strategy perform better than ones using machine-centric strategy. Finally, we provide managerial insights for the problem by analysing the technological parameters of batches, importance of agents, and demand seasonality. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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23. A 3D Parameterized BIM-Modeling Method for Complex Engineering Structures in Building Construction Projects.
- Author
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Yang, Lijun, Gao, Xuexiang, Chen, Song, Li, Qianyao, and Bai, Shuo
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STRUCTURAL engineering ,METHODS engineering ,BUILDING design & construction ,CONSTRUCTION projects ,BATCH processing ,PARAMETRIC modeling - Abstract
The structural components of large-scale public construction projects are more complex than those of ordinary residential buildings, with irregular and diverse components, as well as a large number of repetitive structural elements, which increase the difficulty of BIM-modeling operations. Additionally, there is a significant amount of inherent parameter information in the construction process, which puts forward higher requirements for the application and management capabilities of BIM technology. However, the current BIM software still has deficiencies in the parameterization of complex and irregular structural components, fine modeling, and project management information. To address these issues, this paper takes Grasshopper as the core parametric tool and Revit as the carrier of component attribute information. It investigates the parametric modeling logic of Grasshopper and combines the concepts of parameterization, modularization, standardization, and engineering practicality to create a series of parametric programs for complex structural components in building projects. This approach mainly addresses intricate challenges pertaining to the parametric structural shapes (including batch processing) and parametric structural attributes (including the batch processing of diverse attribute parameters), thereby ensuring the efficiency in BIM modeling throughout the design and construction phases of complex building projects. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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24. Optimizing image processing for modern wide field surveys: enhanced data management based on the LSST science pipelines.
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Hong, Yuanyu, Yang, Chao, Zhang, Miaomiao, Chen, Yanpeng, Liu, Binyang, Luo, Wentao, and Yao, Ji
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DATABASE management ,IMAGE processing ,FIELD research ,BATCH processing ,ELECTRONIC data processing ,DIGITAL image processing ,MIDDLEWARE ,PIPELINES - Abstract
Introduction: In recent decades, numerous large survey projects have been initiated to enhance our understanding of the cosmos. Among these, the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) stands out as a flagship project of the Stage IV cosmology imaging surveys, offering an open-source framework for data management and processing adaptable to various instruments. Methods: In this paper, we introduce the 'obs_mccd' software package, designed to serve as a bridge linking raw data from generic mosaic-CCD instruments to the LSST data management framework. The package also facilitates the deployment of tailored configurations to the pipeline middleware. To validate our data processing pipeline, we processed a batch of realistic data from a commissioning wide-field telescope. Results: We established a prototype of the quality control (QC) system capable of assessing image quality parameters such as PSF size, ellipticity, and astrometric calibration. Our findings indicate that using a fifth-order polynomial for astrometric calibration effectively characterizes geometric distortion, achieving a median average geometric distortion residual of 0.011 pixel. Discussion: When comparing the performance of our pipeline to our inhouse pipeline applied to the same dataset, we observed that our new 'obs_mccd' pipeline offers improved precision, reducing the median average geometric distortion residual from 0.016 pixel to 0.011 pixel. This enhancement in performance underscores the benefits of the obs_mccd package in managing and processing data from wide-field surveys, and it opens up new avenues for scientific exploration with smaller, flexible survey systems complementing the LSST. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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25. Nonlinear Quality‐Related Fault Detection Using Neighborhood Embedding Neural Orthogonal Mapping Algorithm for Batch Process.
- Author
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Liu, Kai, Zhao, Xiaoqiang, Hui, Yongyong, and Jiang, Hongmei
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- *
BATCH processing , *FEATURE extraction , *NEIGHBORHOODS , *GEOTHERMAL ecology - Abstract
Quality‐related fault detection has become a hot research topic in recent years. It is not reliable to measure quality‐related relationships only by mutual information among process variables and single‐quality variables. Frequent alarms for quality‐unrelated faults seriously affect the normal operation of industrial production. At the same time, the strong nonlinearity of the process data leads to the difficulty of feature extraction. In this paper, we propose a fault detection algorithm based on nonlinear quality‐related neighborhood embedding neural orthogonal mapping (QR‐NENOM). First, quality‐related and quality‐unrelated variables are selected by Bayesian fusion mutual information, and the weighted method of mutual information is used to enhance the quality‐related information and suppress the quality‐unrelated information. Second, local manifold information is obtained by reconstructing nearest neighbors of process data, and key features are extracted by the nonlinear method composed of neural network and orthogonal mapping. Then, statistical indicators are established to complete fault detection. Finally, the nonlinear feature extraction ability of NENOM is verified by numerical examples, and the QR‐NENOM algorithm proposed in this paper is applied to the penicillin fermentation process. Comparative experiments show that QR‐NENOM has better detection performance for quality‐related faults and fewer alarms for quality‐unrelated faults. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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26. OPTIMAL BACTERIAL RESOURCE ALLOCATION STRATEGIES IN BATCH PROCESSING.
- Author
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YABO, AGUSTÍN GABRIEL, CAILLAU, JEAN-BAPTISTE, and GOUZÉ, JEAN-LUC
- Subjects
PONTRYAGIN'S minimum principle ,BATCH processing ,RESOURCE allocation ,OPTIMAL control theory - Abstract
The study of living microorganisms using resource allocation models has been key in elucidating natural behaviors of bacteria, by allowing allocation of microbial resources to be represented through optimal control strategies. The approach can also be applied to research in microbial cell factories, to investigate the optimal production of value-added compounds regulated by an external control. The latter is the subject of this paper, in which we study batch bioprocessing from a resource allocation perspective. Based on previous works, we propose a simple bacterial growth model accounting for the dynamics of the bioreactor and intracellular composition, and we analyze its asymptotic behavior and stability. Using optimization and optimal control theory, we study the production of biomass and metabolites of interest for infinite- and finite-time horizons. The resulting optimal control problems are studied using Pontryagin's maximum principle and numerical methods, and the solutions found are characterized by the presence of the Fuller phenomenon (producing an infinite set of switching points occurring in a finite-time window) at the junctions with a second-order singular arc. The approach, inspired by biotechnological engineering, aims to shed light upon the role of cellular composition and resource allocation during batch processing and, at the same time, poses very interesting and challenging mathematical problems. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
27. Collaborative Multiple Players to Address Label Sparsity in Quality Prediction of Batch Processes.
- Author
-
Zhao, Ling, Zhang, Zheng, Zhu, Jinlin, Wang, Hongchao, and Xie, Zhenping
- Subjects
BATCH processing ,SUPERVISED learning ,FORECASTING - Abstract
For decades, soft sensors have been extensively renowned for their efficiency in real-time tracking of expensive variables for advanced process control. However, despite the diverse efforts lavished on enhancing their models, the issue of label sparsity when modeling the soft sensors has always posed challenges across various processes. In this paper, a fledgling technique, called co-training, is studied for leveraging only a small ratio of labeled data, to hone and formulate a more advantageous framework in soft sensor modeling. Dissimilar to the conventional routine where only two players are employed, we investigate the efficient number of players in batch processes, making a multiple-player learning scheme to assuage the sparsity issue. Meanwhile, a sliding window spanning across both time and batch direction is used to aggregate the samples for prediction, and account for the unique 2D correlations among the general batch process data. Altogether, the forged framework can outperform the other prevalent methods, especially when the ratio of unlabeled data is climbing up, and two case studies are showcased to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
28. An iterated local search for customer order scheduling in additive manufacturing.
- Author
-
Zipfel, Benedikt, Neufeld, Janis, and Buscher, Udo
- Subjects
PRODUCTION scheduling ,SETUP time ,CONSUMERS ,SCHEDULING ,METAHEURISTIC algorithms ,BATCH processing - Abstract
This paper studies the customer order scheduling problem in the context of additive manufacturing. The study discusses an integrated problem involving the nesting of parts as well as the scheduling of batches of nested parts onto unrelated parallel machines. A mixed-integer programming model is presented, based on existing formulations from the literature, that integrates different materials and sequence-dependent setup times. Additionally, a metaheuristic based on an iterated local search is proposed for the problem configuration under consideration. Focusing on minimizing the total weighted tardiness of orders, the efficiency of the heuristic approach is evaluated using comprehensive test data. Further, we show the importance of the considered order-related objective by using qualitative analysis. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
29. Validation of split range control algorithm on a laboratory scale batch reactor heating system.
- Author
-
Mishra, Sandipan R. K., Yadav, Eadala Sarath, and Indiran, Thirunavukkarasu
- Subjects
BATCH reactors ,HEATING ,CHEMICAL processes ,BATCH processing ,TEMPERATURE control ,MAXIMUM power point trackers - Abstract
Batch reactor is important in process industry and in batch process to produce high quality products for producing various chemicals in batch processes. It is a process that needs precise temperature control. A fast and speedy control action is always desirable for any type of processes with a consideration that the control logic must be less bulky. In this paper, split range control algorithm is designed along with Proportional Integral controller to achieve good steady state response. The control is simulated for batch scale reactor model and implemented in lab scale batch reactor for real time. This algorithm is used in cases where there is two different mode of control to temperature with no physical interface between two modes. The case given below has heater and a coolant valve. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
30. Scheduling through longest batch process time with mean flow time criterion.
- Author
-
Medikondu, Nageswara Rao, Ratna Prasad, P., Rajak, Upendra, Afzal, Asif, Baig, Maughal Ahmed Ali, and Narendar, Ch.
- Subjects
FLEXIBLE manufacturing systems ,BATCH processing ,WORKING hours ,HEURISTIC algorithms ,SCHEDULING - Abstract
Flexible Manufacturing System [FMS] is an automatic manufacturing gadget which affords a number of sorts of flexibilities like laptop flexibility, routing flexibility, quantity flexibility etc. These flexibilities supply a lot of advantages in phrases of discount in idle time, work in process, device unbalance etc. The existing work offers with scheduling in FMS with a goal of minimizing the imply waft time. Scheduling is a procedure of arranging, controlling and optimizing the work and work load. Two sorts of scheduling algorithm-precedence scheduling (SPT) and Vehicle heuristic algorithm (VHA) are cautioned in this paper. These algorithms are applied in JAVA. Performances of these algorithms are introduced in this paper. It minimizes the suggest glide time and maximizes the computing device utilization. It has been observed that scheduling of machines and AGVs concurrently are a higher scheduling approach for optimizing a variety of overall performance measures in FMS [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
31. Fault-tolerant iterative learning control for batch processes with time-varying state delays and uncertainties.
- Author
-
Chen, Hua, Xu, Rui, Yao, Tang, and Long, Xinyuan
- Subjects
ITERATIVE learning control ,BATCH processing ,LINEAR matrix inequalities ,NONLINEAR estimation ,FAULT-tolerant control systems ,NONLINEAR equations - Abstract
A proportional-derivative (PD)-type iterative learning control (ILC) combined with fault-tolerant control (FTC) for a discrete-time batch process with time-varying delays and uncertainties is presented in this paper. The main goals of paper are to drive the considered output profile to the desired reference with the avoidance of possible process faults, delays and uncertainties. First, the nonlinear estimation problem is simplified to the linear one by introducing an appropriate system reparametrisation to deal with the problem of the simultaneous estimation of state and process fault. Second, to solve the problem of time-varying delays and uncertainties, this paper makes use of the two-dimensional (2-D) and repetitive characteristics of batch processes and introduces the state error, tracking error and estimation error to establish the dynamics model of FTILC that is eventually converted into a linear repetitive process model. Then, sufficient conditions based on the Lyapunov–Krasovskii theory and H ∞ methodology are established to ensure the robust stability along the trial in according with linear matrix inequalities (LMIs) for all uncertainties. Finally, this paper manifests the effectiveness of the proposed sufficient conditions by a simulation of injection moulding process. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
32. Petrochemical condensate treatment by membrane aerated biofilm reactors: A pilot study
- Author
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Arne Verliefde, Emile Cornelissen, Nicolaas Van Belzen, Irina Veleva, Marjolein Vanoppen, and Wout Van Weert
- Subjects
Total organic carbon ,Biochemical oxygen demand ,Hydraulic retention time ,General Chemical Engineering ,General Chemistry ,Pulp and paper industry ,Industrial and Manufacturing Engineering ,Ammonia ,chemistry.chemical_compound ,Petrochemical ,Activated sludge ,chemistry ,Batch processing ,Environmental Chemistry ,Environmental science ,Aeration - Abstract
An OxyMem membrane aerated biofilm reactor (MABR) pilot of two ~ 54 L reactors in series with a hydraulic retention time (HRT) of 10 h each was successfully applied for the treatment of real petrochemical condensate with a total organic carbon (TOC) concentration of ~ 100 mg/L. The main aim was to (i) evaluate the removal efficiency (RE) of main petrochemical pollutants and (ii) to examine the operational performance of the two on-site pilot-scale MABR units. The start-up/inoculation of the reactors in batch mode, followed by continuous pilot operation on synthetic feed, and the transition from synthetic to an actual petrochemical feed were covered. At stable operational conditions on the actual feed, the pilot in series achieved an overall RE for TOC, biological oxygen demand (BOD5), organic acids, phenol, and ammonia of 80–85%, ~95%, >98%, ~98%, and 70–90%. The system in series demonstrated high resilience to process fluctuations, high treatment efficiency of the complex feed, and the ability to develop a diverse biofilm. In terms of oxygen transfer rate (OTR), oxygen transfer efficiency (OTE), and specific aeration efficiency (SAE) values of > 1.5 g/m2/day, > 21%, and > 6.5 kg O2/kWh were achieved. Such values outcompete the aeration efficiency (AE) of conventional activated sludge (CAS) processes and demonstrate the potential of MABRs to achieve higher OTE. Based on the collected data, the MABR concept in series can be considered highly effective for treatment of petrochemical streams.
- Published
- 2022
33. Attention-Based Two-Dimensional Dynamic-Scale Graph Autoencoder for Batch Process Monitoring.
- Author
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Zhu, Jinlin, Gao, Xingke, and Zhang, Zheng
- Subjects
BATCH processing ,PEARSON correlation (Statistics) ,AUTOMATIC control systems - Abstract
Traditional two-dimensional dynamic fault detection methods describe nonlinear dynamics by constructing a two-dimensional sliding window in the batch and time directions. However, determining the shape of a two-dimensional sliding window for different phases can be challenging. Samples in the two-dimensional sliding windows are assigned equal importance before being utilized for feature engineering and statistical control. This will inevitably lead to redundancy in the input, complicating fault detection. This paper proposes a novel method named attention-based two-dimensional dynamic-scale graph autoencoder (2D-ADSGAE). Firstly, a new approach is introduced to construct a graph based on a predefined sliding window, taking into account the differences in importance and redundancy. Secondly, to address the training difficulties and adapt to the inherent heterogeneity typically present in the dynamics of a batch across both its time and batch directions, we devise a method to determine the shape of the sliding window using the Pearson correlation coefficient and a high-density gridding policy. The method is advantageous in determining the shape of the sliding windows at different phases, extracting nonlinear dynamics from batch process data, and reducing redundant information in the sliding windows. Two case studies demonstrate the superiority of 2D-ADSGAE. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
34. Binary-Tree-Fed Mixnet: An Efficient Symmetric Encryption Solution.
- Author
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López-García, Diego Antonio, Pérez Torreglosa, Juan, Vera, David, and Sánchez-Raya, Manuel
- Subjects
CRYPTOGRAPHY ,BATCH processing ,ANONYMITY ,PERMUTATIONS ,ELECTRONIC voting ,WIRELESS sensor network security - Abstract
Mixnets are an instrument to achieve anonymity. They are generally a sequence of servers that apply a cryptographic process and a permutation to a batch of user messages. Most use asymmetric cryptography, with the high computational cost that this entails. The main objective of this study is to reduce delay in mixnet nodes. In this sense, this paper presents a new scheme that is based only on symmetric cryptography. The novelty of this scheme is the use of binary graphs built by mixnet nodes. The root node collects user keys and labels without knowing their owners. After feeding each node by its graph, they can establish a random permutation and relate their keys to the incoming batch positions through labels. The differences with previous symmetric schemes are that users do not need long headers and nodes avoid the searching process. The outcomes are security and efficiency improvements. As far as we know, it is the fastest mixnet system. Therefore, it is appropriate for high-throughput applications like national polls (many users) or debates (many messages). [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
35. An Energy-Efficient Unrelated Parallel Machine Scheduling Problem with Batch Processing and Time-of-Use Electricity Prices.
- Author
-
Feng, Liman, Chen, Guo, Zhou, Shengchao, Zhou, Xiaojun, and Jin, Mingzhou
- Subjects
BATCH processing ,ELECTRICITY pricing ,GREENHOUSE gases ,ENERGY consumption ,LINEAR programming ,INTEGER programming - Abstract
The extensive consumption of energy in manufacturing has led to a large amount of greenhouse gas emissions that have caused an enormous effect on the environment. Therefore, investigating how to reduce energy consumption in manufacturing is of great significance to cleaner production. This paper considers an energy-conscious unrelated parallel batch processing machine scheduling problem under time-of-use (TOU) electricity prices. Under TOU, electricity prices vary for different periods of a day. This problem is grouping jobs into batches, assigning the batches to machines and allocating time to the batches so as to minimize the total electricity cost. A mixed-integer linear programming model and two groups of heuristics are proposed to solve this problem. The first group of heuristics first forms batches, assigns the batches to machines and finally allocates time to the batches, while the second group of heuristics first assigns jobs to machines, batches the jobs on each machine and finally allocates time to each batch. The computational results show that the SPT-FBLPT-P1 heuristic in the second group can provide high-quality solutions for large-scaled instances in a short time, in which the jobs are assigned to the machines based on the shortest processing time rule, the jobs on each machine are batched following the full-batch longest processing time algorithm, and the time is allocated to each batch following an integer programming approach. The MDEC-FBLPT-P1 heuristic that uses the minimum difference of the power consumption algorithm to assign the jobs also performed well. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
36. A new optimisation method of PIDC controller under constraints on robustness and sensitivity to measurement noise using amplitude optimum principle.
- Author
-
Mandić, Petar D., Bošković, Marko Č., Šekara, Tomislav B., and Lazarević, Mihailo P.
- Subjects
CONSTRAINED optimization ,PID controllers ,BATCH processing - Abstract
This paper presents a new optimisation method for PID controller cascaded with a lead-lag compensator (PIDC). Parameters of the controller are obtained by solving the constrained optimisation problem. We propose two variants of the optimality criterion. The first one is defined through the max–min optimisation problem wherein objective function is the amplitude frequency response of the PIDC controller. The second one is based on an effective approximation of the minimum value of the amplitude frequency response of the PIDC controller. Consequently, we obtain a computationally less expensive problem. Both variants of optimality criterion result in efficient load disturbance and noise rejection, while robustness is ensured by constraining the value of the maximum sensitivity Ms. Good reference shaping is supported with proper constraints based on the Amplitude Optimum (AO) principle. Numerous batches of processes typically encountered in the industry are used to demonstrate the effectiveness of the proposed design method. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
37. Adsorption of direct blue 106 dye using zinc oxide nanoparticles prepared via green synthesis technique.
- Author
-
Eleryan, Ahmed, Aigbe, Uyiosa O., Ukhurebor, Kingsley E., Onyancha, Robert B., Hassaan, Mohamed A., Elkatory, Marwa R., Ragab, Safaa, Osibote, Otolorin A., Kusuma, Heri S., and El Nemr, Ahmed
- Subjects
ADSORPTION (Chemistry) ,DYES & dyeing ,ADSORPTION capacity ,ZINC oxide ,BODIES of water ,BATCH processing ,NANOPARTICLES - Abstract
Zinc oxide nanoparticles (ZnO-NPs) have in recent times shown effective adsorption capability for the confiscation of colour contaminants from aqueous environments (aquatic ecosystems or water bodies) due to the fact that ZnO contains more functional groups. Direct blue 106 (DB106) dye was selected for this present study as a model composite due to its wide range of uses in textiles (cotton and wools), woods, and paper industries, as well as their therapeutic applications, along with its potential for impairments. This study therefore focuses on the use of DB106 dye as a model composite due to its wide range of uses in textiles (cotton and wools), woods, and paper industries, as well as their therapeutic applications and their potential for impairments. Furthermore, the surface functionalization, shape, and composite pore size were revealed by TEM, FTIR, UV, and BET techniques. The current study uses green synthesis method to prepare ZnO-NPs as an adsorbent for the DB106 dye molecules adsorption under various conditions using the batch adsorption process. The adsorption of DB106 dye to the ZnO-NPs biosorbent was detected to be pH-dependent, with optimal adsorption of DB106 (anionic) dye particles observed at pH 7. DB106 dye adsorption to the synthesized ZnO-NPs adsorbent was distinct by means of the linearized Langmuir (LNR) and pseudo-second-order (SO) models, with an estimated maximum adsorption capacity (Q
m ) of 370.37 mg/g. [ABSTRACT FROM AUTHOR]- Published
- 2023
- Full Text
- View/download PDF
38. Iterative Learning-Based Predictive Control Method for Electronic Grade Silicon Single Crystal Batch Process.
- Author
-
Wan, Yin, Liu, Ding, and Ren, Jun-Chao
- Subjects
ITERATIVE learning control ,BATCH processing ,SILICON crystals ,SINGLE crystals ,ELECTRONIC control ,PREDICTIVE control systems - Abstract
The growth process of silicon single crystal (SSC) is a typical batch production process, which has the characteristics of batch size, variety, complex process, and intensive technology. In order to accurately control the diameter and thermal field temperature of the Czochralski (Cz) SSC batch production process to ensure that the actual production requirements of electronic-grade SSC are met, this paper proposes an iterative learning-based batch process predictive control strategy Model Predictive Control - Iterative Learning Control - Extended State Observer (MPC-ILC-ESO). The control strategy consists of two parts. The time axis is a dual Model Predictive Control (MPC) controller, which is mainly used to deal with disturbance suppression and constraints in the production process of a single batch of SSC. The iterative axis is the Extended State Observer (ESO) based Iterative Learning Control (ILC) control algorithm to deal with uncertainty and disturbance estimation and compensation in the multi-batch production process. The control effects of the time axis and the batch axis are superimposed as the final controlled object input to ensure the crystal diameter and stable thermal field temperature. Finally, the effectiveness of the proposed control strategy is verified based on the data analysis of the semiconductor industry production process. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
39. A dynamic optimisation approach for a single machine scheduling problem with machine conditions and maintenance decisions.
- Author
-
Yang, Wenhui, Chen, Lu, and Dauzère-Pèrés, Stèphane
- Subjects
PRODUCTION scheduling ,BATCH processing ,MARKOV processes ,MACHINERY ,SCHEDULING - Abstract
In modern production systems, considering machine conditions is becoming essential to achieving an overall optimisation of the production schedule. This paper studies a single machine scheduling problem, where the actual processing times of jobs depend on their position in the production sequence and maintenance is considered. Moreover, the machine is subject to an uncertain condition variation. There is a trade-off between rejecting a maintenance action, resulting in longer processing times, and accepting a maintenance action, leading to higher processing efficiency for future jobs. The problem is formulated as a finite-horizon Markov Decision Process. The objective is to minimise the makespan. Optimality properties are analysed, based on which a dynamic optimisation approach is developed. Computational experiments demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
40. Fungal biopesticide production: Process scale-up and sequential batch mode operation with Trichoderma harzianum using agro-industrial solid wastes of different biodegradability
- Author
-
Arnau Sala, Adriana Artola, Raquel Barrena, and Antoni Sánchez
- Subjects
biology ,Moisture ,General Chemical Engineering ,Trichoderma harzianum ,General Chemistry ,Biodegradation ,biology.organism_classification ,Pulp and paper industry ,Fungal conidia ,Husk ,Industrial and Manufacturing Engineering ,Sequential-batch reactor ,Solid-state fermentation ,SCALE-UP ,Batch processing ,Bioreactor ,Environmental Chemistry ,Packed bed reactor ,Fermentation ,Substrate biodegradability ,Mathematics - Abstract
Altres ajuts: acords transformatius de la UAB This work presents a sequential batch operational strategy (SBR) for fungal conidia production in solid-state fermentation (SSF) to improve the traditional batch operation, while also aiming to present a robust and scalable process. Trichoderma harzianum was fermented using two substrates with different biodegradability (rice husk and beer draff), scaling from 1.5 L to 22 L bioreactors. Before the SBR operation, the optimum time to get inoculum from each SBR batch was determined as 4 days. While single batch process scale-up was successful with both substrates, SBR strategy was only feasible using beer draff as substrate: conidia production was sustained during 3 consecutive batches in 1.5 L bioreactors and for 5 batches at 22 L. At both scales conidia production was around 2.0x109 conidia g-1dm, achieving maximum specific oxygen consumption rate (sOUR) values close to 4 g O2 kg-1dm h-1 in most reactors. Air filled porosity was found as a key parameter regarding process scale-up, with a minimum value of 80% as necessary to proper scaling up to 22 L. Process robustness was statistically demonstrated as no significant differences in conidia production, moisture and pH were found at different reactor heights using both substrates in most 22 L reactors tests. Consequently, SBR operation has been presented as a reproducible method to overcome traditional packed-bed drawbacks while also improving SSF performance in comparison to traditional industrial SSF processes.
- Published
- 2021
41. Producing biogas from cow manure, chicken manure, and organic waste by batch system
- Author
-
E Kusdiyantini, M Taufiq, and J Windarta
- Subjects
Biogas ,Batch processing ,Environmental science ,Chicken manure ,Biodegradable waste ,Pulp and paper industry ,Cow dung - Abstract
Cow manure, chicken manure, and organic waste are solid wastes that can be used as an energy source through a biogas digester. This study aims to determine the production of biogas from waste in a batch digester system. This research uses a digester in the form of a plastic drum with a capacity of 220 liters. 3 treatments were consisting of digester 1 containing cow manure and water (50:50), digester 2 in the form of a mixture of cow manure, chicken manure, and water (30:20:50), digester 3 containing cow manure, organic waste, and water (30:20:50). The digester is then closed tightly for 130 days. The results showed that the daily temperature ranged between 26°−31°C and the acidity (pH) 7-7.62. The biogas production of each digester was then carried out a flame test with a biogas stove. The results of the gas stove flame test show that digester 2 has a longer total flame period of 4302 seconds, then digester 1 has a total flame period of 4034 seconds and lastly, digester 3 has a total flame period of 2370 seconds.
- Published
- 2021
42. Continuous flow production of xylooligosaccharides by enzymatic hydrolysis
- Author
-
Anil B. Vir, Antonio F. Patti, Gil Garnier, Debjani Ghosh, and Joanne Tanner
- Subjects
Chemistry ,Applied Mathematics ,General Chemical Engineering ,Xylan (coating) ,Biomass ,02 engineering and technology ,General Chemistry ,021001 nanoscience & nanotechnology ,Pulp and paper industry ,Industrial and Manufacturing Engineering ,Hydrolysis ,Flow conditions ,020401 chemical engineering ,Enzymatic hydrolysis ,Batch processing ,Fine chemical ,0204 chemical engineering ,Microreactor ,0210 nano-technology - Abstract
Xylooligosaccharides (XOS) are naturally occurring high value biochemicals with potential applications in the food, pharmaceutical and fine chemical industries. They are typically produced and purified from a variety of biomass resources in expensive and inefficient multi-step batch processes, and this high cost of production is a significant barrier to their commercial adoption. A flow-type microreactor was used to intensify and increase the efficiency of enzymatic XOS production from beechwood xylan. The xylan hydrolysis performance was compared with the same reaction performed under typical batch conditions. Over 80 g XOS per 100 g xylan was obtained in less than one minute of residence time at the optimum conditions tested in continuous flow experiments. The enzymatic xylan hydrolysis performance was improved and xylan to XOS conversion was three times higher under optimised flow conditions compared to a conventional batch process.
- Published
- 2021
43. Real-time performance investigation of a downdraft gasifier fueled by cotton stalks in a batch-mode operation
- Author
-
Ayman I. Bakry, Mahmoud Kadous, Karim Rabea, Magda K. El-Fakharany, and A. H. Khalil
- Subjects
Energy recovery ,Wood gas generator ,business.industry ,020209 energy ,General Chemical Engineering ,Organic Chemistry ,Energy conversion efficiency ,Energy Engineering and Power Technology ,02 engineering and technology ,Raw material ,Pulp and paper industry ,Renewable energy ,Fuel Technology ,020401 chemical engineering ,Yield (chemistry) ,0202 electrical engineering, electronic engineering, information engineering ,Batch processing ,Environmental science ,Heat of combustion ,0204 chemical engineering ,business - Abstract
The gasification of agricultural biomass wastes is considered to be a promising technique for energy recovery from a renewable energy source. The dynamic behavior and performance of an Imbert downdraft gasifier integrated with internal heating of the gasifying agent is investigated. The gasifier is operated by a batch of cotton stalks as the feedstock and using air as the gasifying agent. The air flow rate was changed from 60 up to 200 l/min which is corresponding to equivalence ratio (ER) between 0.305 and 0.195. The real-time measurements of the temperature along the reactor and the analysis of the produced gas during the gasification process were used to study the behavior of the gasification system. The process was evaluated in terms of the lower heating value (LHV), gasification efficiency, carbon conversion efficiency, and the gas yield. The obtained results showed that the gasifier achieved high energy conversion efficiency of cotton stalks compared to the results reported in the literature. An optimum average LHV, gas yield, and cold gas efficiency of 4.61 MJ/m3, 1.94 m3/kg, and 57.4%, respectively, were obtained at ER of 0.235. Besides, the detailed study of the actions inside the gasifier showed the interaction between the different gasification zones and shed the light on the channeling problem of the Imbert gasifier.
- Published
- 2021
44. Adsorption of lead by waste rubber tires from aqueous solution.
- Author
-
Belkhade, Durga S., Dudhe, Pallavi N., Sudame, Aruna M., Ghodmare, Sujesh D., and Khedikar, I. P.
- Subjects
- *
RUBBER waste , *WASTE products , *LEAD , *TIRES , *BATCH processing - Abstract
The environmental effects began with discarding waste tires on earth, and the effects of metals in water are of great worry. The idea is to use a waste raw material to resolve distinct waste materials that are hazardous to human health and the ecosystem. In this paper, waste rubber tires are utilized for the elimination of lead ions from liquids. In the batch process, the effects of lead ion concentration, pH, contact time, and adsorbent dosage were examined, and the elimination of lead ions was optimized. The high lead ion removal efficiency was achieved at a pH of 7, a contact time of 90 minutes, lead concentrations of 10 ppm, and an adsorbent dosage of 2.5 g/100 ml. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. A comprehensive study and review of tuning the performance on database scalability in big data analytics.
- Author
-
Sundarakumar, M.R., Mahadevan, G., Natchadalingam, R., Karthikeyan, G., Ashok, J., Manoharan, J. Samuel, Sathya, V., and Velmurugadass, P.
- Subjects
DATABASES ,BIG data ,DATA libraries ,PYTHON programming language ,BATCH processing ,ELECTRONIC data processing - Abstract
In the modern era, digital data processing with a huge volume of data from the repository is challenging due to various data formats and the extraction techniques available. The accuracy levels and speed of the data processing on larger networks using modern tools have limitations for getting quick results. The major problem of data extraction on the repository is finding the data location and the dynamic changes in the existing data. Even though many researchers created different tools with algorithms for processing those data from the warehouse, it has not given accurate results and gives low latency. This output is due to a larger network of batch processing. The performance of the database scalability has to be tuned with the powerful distributed framework and programming languages for the latest real-time applications to process the huge datasets over the network. Data processing has been done in big data analytics using the modern tools HADOOP and SPARK effectively. Moreover, a recent programming language such as Python will provide solutions with the concepts of map reduction and erasure coding. But it has some challenges and limitations on a huge dataset at network clusters. This review paper deals with Hadoop and Spark features also their challenges and limitations over different criteria such as file size, file formats, and scheduling techniques. In this paper, a detailed survey of the challenges and limitations that occurred during the processing phase in big data analytics was discussed and provided solutions to that by selecting the languages and techniques using modern tools. This paper gives solutions to the research people who are working in big data analytics, for improving the speed of data processing with a proper algorithm over digital data in huge repositories. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
46. Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility.
- Author
-
Gedda, Rebecca, Beilina, Larisa, and Ruomu Tan
- Subjects
CHANGE-point problems ,MULTIPHASE flow ,POINT processes ,COST functions ,ELECTRONIC data processing ,BATCH processing ,TAGS (Metadata) - Abstract
Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner. In the context of process data analytics, change points in the time series of process variables may have an important indication about the process operation. For example, in a batch process, the change points can correspond to the operations and phases defined by the batch recipe. Hence identifying change points can assist labelling the time series data. Various unsupervised algorithms have been developed for change point detection, including the optimisation approach which minimises a cost function with certain penalties to search for the change points. The Bayesian approach is another, which uses Bayesian statistics to calculate the posterior probability of a specific sample being a change point. The paper investigates how the two approaches for change point detection can be applied to process data analytics. In addition, a new type of cost function using Tikhonov regularisation is proposed for the optimisation approach to reduce irrelevant change points caused by randomness in the data. The novelty lies in using regularisation-based cost functions to handle ill-posed problems of noisy data. The results demonstrate that change point detection is useful for process data analytics because change points can produce data segments corresponding to different operating modes or varying conditions, which will be useful for other machine learning tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
47. Techniques and Materials for High‐Throughput Fabrication and Integration of Multiparameter Flexible Skin Patches.
- Author
-
Huang, Xian, Li, Xueting, Wu, Ziyue, Zhou, Pan, Ling, Wei, Wan, Chunxue, and Li, Jiameng
- Subjects
TRANSDERMAL medication ,OXYGEN in the blood ,BATCH processing ,FLEXIBLE electronics ,SCREEN process printing - Abstract
The development of batch fabrication techniques for flexible skin patches may promote the use of these devices in daily health monitoring and public health crisis. Here, a modularized mass fabrication and selective integration approach that can yield multiparameter electronic skin patches are presented. The screen‐printing technique has been adopted to achieve five modularized sensors for electrocardiogram (ECG), temperature, hydration, blood oxygen, and pH sensing as well as a universal central hub to connect the sensors with external wireless circuits. In a single glass substrate, 13 ECG electrodes, more than 200 sensors, or 80 central hubs can be printed with high homogeneity in performance. In addition to rapid printing of these components, special temperature‐sensitive paste, batch electropolymerization processes, selection of stress confining substrates, and usage of anisotropic conductive tapes have been investigated to facilitate the integration processes of the multiparameter skin patches. The systematic techniques presented in this paper may help to overcome the bottleneck that is currently constraining the mass fabrication of multiparameter skin patches, allowing rapid deployment and convenient application of these devices for health monitoring in large populations. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
48. Reducing total annual cost and CO2 emissions in batch distillation for separating ternary wide boiling mixtures using vapor recompression heat pump.
- Author
-
Gandu, Radhika, Burolia, Akash Kumar, Ambati, Seshagiri Rao, and Gara, Uday Bhaskar Babu
- Subjects
HEAT pumps ,DISTILLATION ,GASES ,VAPORS ,EBULLITION ,BATCH processing - Abstract
This paper presents cost-effective heat pump assisted vapor recompression (VRC) design algorithms for the separation of ternary wide boiling mixture in batch distillation in order to reduce total annual cost (TAC) and carbon dioxide (CO
2 ) emissions. A minimum TAC and CO2 is required by the batch distillation process industry for any investments in heat integrated systems, such as VRC. Consequently, the design conditions for implementing VRC should be chosen such that the energetic performance is maximum at minimum TAC. The model system selected in this paper is an application involving high temperature lift, that is, hexanol–octanol–decanol ternary wide boiling mixture. First, a systematic simulation algorithm was developed for conventional multicomponent batch distillation (CMBD) and single-stage vapor recompressed multicomponent batch distillation (SiVRMBD) to determine the optimal number of stages based on the maximum TAC savings. The SiVRMBD saves more energy and TAC than CMBD. However, SiVRMBD has a high compression ratio (CR) throughout the operation, which is not practically feasible for the batch distillation processing. Second, in order to increase the performance and minimize the SiVRMBD weakness, a novel optimal multi-stage vapor recompression algorithm was proposed to operate at the lowest possible CR (<3.5) throughout the batch operation while also conserving the most TAC. Overall, the findings suggest that the proposed optimal multi-stage VRC reduces TAC and CO2 emissions significantly when compared to CMBD. Finally, the influence of the different feed compositions on VRC performance is also studied. [ABSTRACT FROM AUTHOR]- Published
- 2023
- Full Text
- View/download PDF
49. Two-Stage Hybrid Optimization Algorithm for Silicon Single Crystal Batch Scheduling Problem under Fuzzy Processing Time.
- Author
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Kang, Lu, Liu, Ding, Wu, Yali, and Ping, Guozheng
- Subjects
SILICON crystals ,SINGLE crystals ,MATHEMATICAL optimization ,PRODUCTION scheduling ,BATCH processing ,DIFFERENTIAL evolution - Abstract
Considering the widely existing processing time uncertainty in the real-world production process, this paper constructs a fuzzy mathematical model for the silicon single crystal production batch scheduling problem to minimize the maximum completion time. In this paper, a two-stage hybrid optimization algorithm (TSHOA) is proposed for solving the scheduling model. Firstly, the improved differential evolution algorithm (IDE) is used to solve the order quantity allocation problem of silicon single crystal with different sizes to obtain the quantity of silicon single crystal rods with different sizes produced by different types of single crystal furnaces. Secondly, the variable neighborhood search (VNS) algorithm is adopted to optimize the order quantity sequencingof batch production processes. Finally, simulations and comparisons demonstrate the feasibility of the model and the effectiveness of TSHOA. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
50. Mining Batch Activation Rules from Event Logs.
- Author
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Martin, Niels, Solti, Andreas, Mendling, Jan, Depaire, Benoit, and Caris, An
- Abstract
Batch processing refers to an organization of work in which cases are synchronized such that they can be processed as a group. Prior research has studied batch processing mainly from a deductive angle, trying to identify optimal rules for composing batches. As a consequence, we lack methodological support to investigate according to which rules human resources build batches in work settings where batching rules are not strictly enforced. In this paper, we address this research gap by developing a technique to inductively mine batch activation rules from process execution data. The obtained batch activation rules can be used for various purposes, including to explicate the real-life batching behavior of human resources; to determine the compliance between the prescribed and actual batching behavior; or to investigate the influence of alternative batching behavior on service levels. The evaluation of our technique using both synthetic and real-world data demonstrates its effectiveness. With this work we complement prescriptive research on batch processing with a descriptive technique that is empirically grounded in process execution data. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
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