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The various types of biorefractory organic pollutants present in pulp and paper mill effluent is obnoxious to the environment and severely affects the biotic and abiotic components. The present study investigates the isolation and characterization of a novel strain isolated from pulp and paper mill effluent. Among the isolates, the strain Bacillus sp. IITRDVM-5 showed maximum degradation and decolorization of 100% filter sterilized effluent, supplemented with only nitrogen and phosphorus in a sequential batch treatment. Under optimized conditions, the maximum reduction in TOC, COD, BOD and color was 82.22, 89.50, 93.33 and 73.01%, respectively after 72 h treatment. In addition, the reduction in total phenol, AOX and lignin was 88.5, 75.63 and 64.10%, respectively. The UV–Vis spectral scan identified the degradation pattern of contaminants during treatment. FTIR and GC-MS analysis indicated significant change in functional groups composition of organic pollutants and confirmed their transformation after treatment. Significant degradation of phenolic and chlorolignin derivatives was observed through GC-MS. Furthermore, the evaluation of toxicity with V. radiata and T. foenum-graecum seeds confirmed the toxic nature of effluent, of which detoxification was observed post bacterial treatment. The results highlight the degradation and decolorization potential of Bacillus sp. IITRDVM-5 and its future application in pulp and paper effluent treatment. Display Omitted • The paper mill effluent treatment was done without addition of buffer or additional carbon source. • 88.5, 75.63, 64.10, 82.22 and 73.01% reduction in total phenol, AOX, lignin, TOC and color was observed. • UV–Vis scan, FTIR and GC-MS confirmed the degradation of lignin and chlorolignin derivatives. • Detoxification of effluent post treatment was confirmed by germination in V. radiata and T. foenum-graecum seeds. [ABSTRACT FROM AUTHOR]

Highlights: [•] Kinetics of volatile solids biodegradation in batch biodrying was studied. [•] The effect of air flow-rate (AFR) on the process was evaluated. [•] Five kinetic models were analyzed with two statistical indicators. [•] Higher AFR produces higher moisture content reduction and lower VS reduction. [•] Satisfactory fitting of kinetic model to the experimental data of VS was achieved. [ABSTRACT FROM AUTHOR]

Paper sludge (PS) from the paper and pulp industry consists primarily of cellulose and ash and has significant potential for ethanol production. Thirty-seven PS samples from 11 South African paper and pulp mills exhibited large variation in chemical composition and resulting ethanol production. Simultaneous saccharification and fermentation (SSF) of PS in fed-batch culture was investigated at high solid loadings and low enzyme dosages. Water holding capacity and viscosity of the PS influenced ethanol production at elevated solid loadings of PS. High viscosity of PS from virgin pulp mills restricted the solid loading to 18% (w/w) at an enzyme dosage of 20 FPU/gram dry PS (gdPS), whereas an optimal solid loading of 27% (w/w) was achieved with corrugated recycle mill PS at 11 FPU/gdPS. Ethanol concentration and yield of virgin pulp and corrugated recycle PS were 34.2 g/L at 66.9% and 45.5 g/L at 78.2%, respectively. [ABSTRACT FROM AUTHOR]

Distributed Scatterers Interferometry (DS-InSAR) has been widely applied to increase the number of measurement points (MP) in complex mountainous areas with dense vegetation and complicated topography. However, DS-InSAR method adopts batch processing mode. When new observation data acquired, the entire archived data is reprocessed, completely ignoring the existing results, and not suitable for high-performance processing of operational observation data. The current research focuses on the automation of SAR data acquisition and processing optimization, but the core time series analysis method remains unchanged. In this paper, based on the traditional Sequential Estimator proposed by Ansari in 2017, a Recursive Sequential Estimator with Flexible Batches (RSEFB) is improved to divide the large dataset flexibly without requirements on the number of images in each subset. This method updates and processes the newly acquired SAR data in near real-time, and obtains long-time sequence results without reprocessing the entire data archived, helpful to the early warning of landslide disaster in the future. 132 Sentinel-1 SAR images and 44 TerraSAR-X SAR images were utilized to inverse the line of sight (LOS) surface deformation of Xishancun landslide and Huangnibazi landslide in Li County, Sichuan Province, China. RSEFB method is applied to retrieve time-series displacements from Sentinel-1 and TerraSAR-X datasets, respectively. The comparison with the traditional Sequential Estimator and validation through Global Position System (GPS) monitoring data proved the effectiveness and reliability of the RSEFB method. The research shows that Xishancun landslide is in a state of slow and uneven deformation, and the non-sliding part of Huangnibazi landslide has obvious deformation signal, so continuous monitoring is needed to prevent and mitigate possible catastrophic slope failure events. [ABSTRACT FROM AUTHOR]

Data-driven based batch process monitoring is of critical importance in ensuring stable operating processes and consistent product quality. For long-duration batch processes, it is unrealistic to involve expensive data to train a statistical model for monitoring. To model the inherently batch-wise and variable-wise dynamics, nonlinearity, and time-varying characteristics, this paper proposes a local learning-based two-dimensional subspace identification (LL-2D-SID) scheme based on the similarity between the ongoing batch and the previous batches. The similarity is estimated by the extended extrapolative time-warping. Unlike the conventional statistical models using rich batch data, LL-2D-SID through online optimizing mechanism using limited batch data still has good prediction performance. The application of the sintering process in the polytetrafluoroethylene production has demonstrated that the LL-2D-SID based process monitoring scheme can not only accurately track temperature changes but also timely give fault alarms with a lower error alarm rate than the other SID-based process monitoring schemes. • Local learning-based 2-dimensional subspace identification (LL-2D-SID) is proposed. • Limited batch data of batch processes can be used to train LL-2D-SID. • Extended XTW is introduced to estimate similarity of data between current and previous batches. • Modeling data changes are integrated into SID by updating projection matrix. • Efficient implementation ensures LL-2D-SID to online monitor PTFE process. [ABSTRACT FROM AUTHOR]

To solve some scheduling problems of batch processes based on timed Petri net models, timed extended reachability graphs (TERGs) and approximated TERGs can be used. Such graphs abstract temporal specifications and represent parts of timed languages. By exploring the feasible trajectories in a TERG, optimal schedules can be obtained with respect to the makespans of batch processes that are modeled by timed Petri nets. Nevertheless, the rapid growth of the number of states in a TERG makes the problem intractable for large systems. In this paper, we improve the existing clustering TERG approach, and we make it suitable for large sized batch processes. We also enlarge a systematic approach to model batch processes with timed Petri nets. Finally, a comprehensive example of scheduling problem is studied for an archetypal chemical production plant in order to illustrate the efficiency of the proposed approach. • The scheduling issue of batch chemical processes is formalized with timed Petri nets. • A graph named ClusTERG approximates the state space through hierarchical clustering. • The approximation error of this ClusTERG is far less than other approximations. • Such an approach is used to obtain near-optimal schedules of a batch process. [ABSTRACT FROM AUTHOR]

Fault diagnosis plays a pivotal role in identifying the root causes of a fault. Current fault diagnosis methods encounter the shortcomings being unable to assess the fault amplitude or having low efficiency for batch fermentation process. In order to solve the above problems, this paper proposes a fault detection model named convolutional neural network based on variational autoencoder (CNN-VAE) and a fault diagnosis based on counterfactual inference (FDCI). To begin with, quality-related process variables are selected using mutual information (MI). Next, a two-dimensional moving window is used to obtain input sequences from the process data. Then, two statistics from the latent and residual domains of the CNN-VAE model are constructed for fault detection. Additionally, once a fault occurs, FDCI is used to locate the root cause of a fault. Finally, a simulation process and a real-world L. plantarum batch fermentation process are provided to demonstrate the effectiveness of the proposed approache. • A novel model named CNN-VAE is proposed for batch fermentation processes monitoring. • An innovative fault diagnosis strategy named FDCI is proposed. • A simulation and an experiment verify the efficiency of the CNN-VAE and FDCI strategy. [ABSTRACT FROM AUTHOR]

The design of batch food processing plants is complex which requires the use of advanced simulation and optimisation methods. This paper presents how existing methods for multi-scale design and multi-objective optimisation can be combined together while carrying out the integrative design of these complex systems. To implement the Methodology for the Integrated, Multi-scale and Multi-Objective Design of Systems (MIMMODS), 8 tasks must be completed. The goal of the first 6 tasks is to build a design support tool that (i) simulates the behaviour of the processing plant and estimates its performance at different functional levels and whatever design variables it is made up of, (ii) takes into account the preferences of the decision-maker to convert the performances of a design solution into desirability indices and (iii) searches for the design solution(s) that best satisfies the design objectives and the preferences of the decision-maker. When the design support tool is functioning, the results have to be visualised and analysed in order to validate whether the resulting design solution is satisfactory, realistic, feasible and consistent or whether a new iteration of the methodology needs to be conducted to refine the design support tool. [ABSTRACT FROM AUTHOR]

This paper proposes an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme for a class of nonlinear batch processes with a priori P-type feedback control structure subject to nonrepetitive uncertainties, by only using the process input and output data available in practice. Firstly, the unknown process dynamics is equivalently transformed into an iterative dynamic linearization data model (IDLDM) with a residual term. A radial basis function neural network is adopted to estimate the pseudo partial derivative information related to IDLDM, and meanwhile, a data-driven iterative ESO is constructed to estimate the unknown residual term along the batch direction. Then, an adaptive set-point learning control law is designed to merely regulate the set-point command of the closed-loop control structure for realizing batch optimization. Robust convergence of the output tracking error along the batch direction is rigorously analyzed by using the contraction mapping approach and mathematical induction. Finally, two illustrative examples from the literature are used to validate the effectiveness and advantage of the proposed design. • Novel extended state observer (ESO) based data-driven set-point learning control (DDSPLC). • Iterative dynamic linearized data model (IDLDM) for nonlinear batch processes. • Use the radial basis function neural network (RBFNN) to approximate pseudo partial derivative. • Set-point learning law with an adaptive learning gain for a priori P-type feedback structure. • Robust convergence of the resulting ILC system along the batch direction is rigorously analyzed. [ABSTRACT FROM AUTHOR]

In this paper, a new off-policy two-dimensional (2D) reinforcement learning approach is proposed to deal with the optimal tracking control (OTC) issue of batch processes with network-induced dropout and disturbances. A dropout 2D augmented Smith predictor is first devised to estimate the present extended state utilizing past data of time and batch orientations. The dropout 2D value function and Q-function are further defined, and their relation is analyzed to meet the optimal performance. On this basis, the dropout 2D Bellman equation is derived according to the principle of the Q-function. For the sake of addressing the dropout 2D OTC problem of batch processes, two algorithms, i.e., the off-line 2D policy iteration algorithm and the off-policy 2D Q-learning algorithm, are presented. The latter method is developed by applying only the input and the estimated state, not the underlying information of the system. Meanwhile, the analysis with regard to the unbiasedness of solutions and convergence is separately given. The effectiveness of the provided methodologies is eventually validated through the application of a simulated case during the filling process. • The 2D model-free control method can solve a dropout 2D OTC problem for the batch processes. • The 2D Q-learning method can find the optimal gain of the control law better. • The analysis of unbiasedness and convergence are given by the rigorous theoretical proof. • The proposed method for dropout OTC has the merit to exempt the effect of the probing noises and to produce unbiased solution. [ABSTRACT FROM AUTHOR]

This paper proposes a latent variable nonlinear iterative learning model predictive control method (LV-NILMPC) based on the dynamic fuzzy partial least squares (DFPLS) model to achieve trajectory tracking and process disturbance suppression in multivariable nonlinear batch processes. The dynamic and nonlinear characteristics of the physical system are constructed by integrating the T-S fuzzy model into the regression framework of the dynamic partial least squares (PLS) inner model. The decoupling and dimensionality reduction characteristics of the DFPLS model automatically decompose a multivariable nonlinear system into multiple univariate subsystems operating independently in the latent variable space. Based on the DFPLS model, we design LV-NILMPC controllers corresponding to each latent variable subspace to track the projection of the reference trajectories. Compared with the previous control method, the method proposed in this paper has a faster convergence rate and smaller tracking error. The method is suitable for nonlinear, multivariable and strong coupling batch processes. Finally, the application of two cases shows that the method is effective. • We extend the iterative learning model predictive control to nonlinear processes. • Partial least squares is employed to decouple the strongly coupled system. • The controller is designed in the latent variable space rather than the original space. • It has smaller tracking error and faster convergence speed than other methods. [ABSTRACT FROM AUTHOR]

Apache Hadoop is an open-source software library which integrates a wide variety of software tools and utilities to facilitate the distributed batch processing of big data sets. Hadoop ecosystem comprises two major components - Hadoop Distributed File System (HDFS), which is primarily used for storage and MapReduce, which is primarily used for processing of the files. The performance of Hadoop holds back when it comes to storage and processing of small files. Small files are essentially the files that are significantly smaller in size when compared to the default block size of HDFS. This is because each small file consumes a block individually leading to excessive memory requirement, access time and processing time. Scaling the memory, allowing access latencies and processing delays beyond a limit is not an option. Henceforth, in this paper, a Systematic Literature Review has been performed to provide a comprehensive and exhaustive gestalt of the small files' problem in Hadoop. The paper defines a comprehensive taxonomy of Hadoop ecosystem and its' existent small files problem. Further, the study also attempts to critically analyze the solutions that have been proposed to overcome this problem. These solutions have been analytically studied to identify the set of parameters that should be considered in impending while proposing solutions pertaining to this problem. [ABSTRACT FROM AUTHOR]

Equipment degradation is ubiquitous in the Chemical Process Industry (CPI), causing significant losses in efficiency, controllability, and plant economy, as well as an increased environmental fingerprint and additional operational safety risks. The case of fouling in heat exchangers, in particular, is well-known and pervasive but still hard to cope with, given the complexity of the underlying mechanisms and the difficulty of assessing its extension in real-time. This problem becomes even more complex in batch processes producing different products, where multiple recipes are used, bringing additional variability and new challenges to the analysis. In this work, we propose a functional data-driven approach for streamlining the analysis and monitoring of the progression of fouling taking place in heat exchangers in multiproduct batch processes. With the approach developed and presented in this paper, process analysis can be efficiently conducted by integrating historical data with engineering knowledge. Furthermore, a surrogate measure of fouling extension in heat exchangers is proposed, that can be readily implemented as an equipment health indicator (EHI) leading to a safer operation of the heat exchanger. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) is an artificial intelligence algorithm that can learn adaptive optimal control law online. In view of the fact that the previous control approaches were usually overly dependent on the model parameters of system, and most existing RL methods are based on state feedback, their application in actual industrial production is limited. Additionally, developing accurate process system models and ensuring the closed-loop system's control performance is more challenging, as modern businesses place a premium on product quality and economic efficiency. As a result, this work introduces a novel data-driven two-dimensional (2D) off-policy Q -learning method based on output feedback is used to achieve optimal tracking control for batch process. First, the error between the actual output and the given set-point is extended to the system to ensure the good tracking performance. Second, by analyzing the relationship between the value function and the Q -function obtained from the 2D system's performance index, the 2D Bellman equation is obtained in terms of output feedback that is independent of the model parameters. The optimal control problem can be effectively solved by the proposed method in this paper when the policy iteration is executed using only the measurement data of system along the batch and time directions. Following that, the proposed approach's unbiasedness and convergence are strictly confirmed. Finally, the simulation results for the injection molding process demonstrate that the proposed method is capable of determining the optimal control law as the number of batches is growing increasingly. • Compared to the 1D Q-learning-based output feedback control method, the control law gain obtained by the proposed method has a smaller deviation and a faster convergence speed. • In contrast to a recent 2D method proposed for solving optimal tracking control problems based on state feedback, the method described in this paper can effectively resolve the problem of unmeasurable states encountered in industrial production process. • Not only does the proposed 2D RL have the merit in immunity to detection noise for the obtained solution, but it also eliminates the need for a state matrix during the learning process. [ABSTRACT FROM AUTHOR]

Self-optimizing control is a strategy for selecting controlled variables, where the economic objective guides the selection and design of controlled variables, with the expectation that maintaining the controlled variables at constant values can achieve optimization effects, translating the process optimization problem into a process control problem. Currently, self-optimizing control is widely applied to steady-state optimization problems. However, the development of process systems exhibits a trend towards refinement, highlighting the importance of optimizing dynamic processes such as batch processes and grade transitions. This paper formally introduces the self-optimizing control problem for dynamic optimization, termed the dynamic self-optimizing control problem, extending the original definition of self-optimizing control. A novel concept, "dynamic controlled variables" (DCVs), is proposed, and an implicit control policy is presented based on this concept. The paper theoretically analyzes the advantages and generality of DCVs compared to explicit control strategies and elucidates the relationship between DCVs and traditional controllers. Moreover, this paper puts forth a data-driven approach to designing self-optimizing DCVs, which considers DCV design as a mapping identification problem and employs deep neural networks to parameterize the variables. Three case studies validate the efficacy and superiority of DCVs in approximating multi-valued and discontinuous functions, as well as their application to dynamic optimization problems with non-fixed horizons, which traditional self-optimizing control methods are unable to address. • The concept of dynamic controlled variables (DCVs) is proposed. • An implicit control policy based on DCVs is proposed. • It is theoretically proven that the implicit policy can represent the set-valued mapping. • Dynamic self-optimizing control is systematically and formally proposed for the first time. • A data-driven method for designing self-optimizing dynamic controlled variables is proposed. [ABSTRACT FROM AUTHOR]

This paper describes a study on PEM balanced-pressure electrolysis in a batch process. For this purpose, a test bench is developed that enables electrolysis to be carried out at high pressure level. In this study, results up to a pressure level of 110 bar are shown. The focus is on the measurement of hydrogen crossover as a function of pressure and current density. An analysis is also conducted on the pressure dependence of the cell voltage. In addition, the catalyst coated membrane (CCM) is examined for ageing effects using SEM and EDX analysis. Taking the measurement results into account, a system model is created in order to determine the limits of balanced-pressure electrolysis in the batch process. • The hydrogen crossover is significantly lower in the batch process than in stationary operation. • For current densities >3 A cm−2, it was shown that the overvoltage decreases as a function of the pressure. • The EDX and SEM -analyses of the pressurised membrane show clear changes. • In the single-stage batch process, a maximum pressure of 140 bar can be achieved. [ABSTRACT FROM AUTHOR]

The accuracy consistency of the journal of the same batch of workpieces directly affects the interchangeability of products, cylindrical grinding is an important means to form the final machining accuracy of workpiece journals. Due to the grinding process system is susceptible to interference, for a given grinding system, the existing model cannot be used to evaluate the consistency of roundness error in the same batch grinding process. In this paper, the vibration coupling model of the cylindrical grinding process is established, and the iterative algorithm of "grinding force-instantaneous grinding depth" is proposed. On this basis, the grinding parameters are reflected in the coupling model of the grinding system with random characteristics within the design range, and the roundness error distribution of the same batch of workpiece journals after grinding is simulated by the Monte Carlo method to form a statistical analysis method of roundness error batch consistency. The validity of the proposed grinding roundness error consistency analysis method is confirmed by the corresponding grinding test. Finally, the influence of the grinding parameters on the consistency of roundness error after grinding is analyzed, and the research conclusions can provide reference for the design of grinding quality consistency. [Display omitted] • The consistency analysis model and algorithm of grinding roundness error of workpiece journal are proposed. • The rationality of the proposed model is verified by experiments. • The Influence of roundness error batch consistency of workpiece journal grinding is studied. [ABSTRACT FROM AUTHOR]

This paper concerns with the JY-KPLS model based transfer learning for the operation optimization of nonlinear batch processes. Due to problems of data insufficiency and uncertainties in a new nonlinear batch process that has just been put into production, the model-(new) process mismatch is usually inevitable, which is also the main reason for the poor performance of the batch process. To solve this problem, this paper first adopts the JY-KPLS model to capture the behavior of the nonlinear batch process, and takes full advantage of the information in similar batch processes to assist the modeling and operation optimization of a new process. Then, a data selection based batch-to-batch optimization control strategy is proposed in this paper to reduce the adverse effects of this mismatch on the operation of the new batch process. Finally, the feasibility of the proposed method is demonstrated by simulations. • The optimization of the batch process with strong nonlinearity and scarce process data is realized. • JY-KPLS transfer model is applied to nonlinear batch process operation optimization. • NCO mismatch problem is analyzed and reduced by model updating and data selection. • The algorithm framework of JY-KPLS model based batch-to-batch optimization is given. [ABSTRACT FROM AUTHOR]

Paper discusses modern methods for localization of mobile ground robot in outdoor environment. Special attention is payed to lidar-based methods which allow to do precise simultaneous localization and mapping (SLAM) independently to lighting conditions. Modular approach to lidar-based localization is proposed. It consists of three modules: map reconstructor, map merger and corrector, localization module. As map reconstructor we use 3D Lidar-based Graph SLAM, state-of-the-art method LOAM and its modification with point cloud segmentation techniques A-LeGO-LOAM. As map merger and corrector we apply the technique of interactive map correction for 3D Graph SLAM. For localization module we compare different real-time approaches: NDT scan matching and Localization with scan batch processing. They do not allow to localize the robot with an unknown initial position. To overcome this problem, a stage of global Monte Carlo localization is proposed, which allows to generate a switching condition and a rough initial pose. Matrix of localization convergence speed is introduced to study influence of possible inaccuracies of initial pose estimation to the convergence of real-time localization approach. An important emphasis in the paper is made to the evaluation of the localization quality with relative and absolute metrics. Experimental results are obtained for own data from mobile ground robot ClearPath Husky equipped with Velodyne HDL-32E lidar. Proposed approach to robot localization achieves real-time performance for different hardware platforms in particular single-board computer Nvidia Jetson Xavier. It demonstrates possibility for integration of studied modular localization approach to onboard control systems of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Batch processes usually adopt a two-layer architecture control strategy to reduce the calculation cost while ensuring economic performance. The upper layer in the strategy obtains the nominal optimal reference trajectory by solving an economic optimization problem, while the lower MPC one possesses the task of tracking this trajectory as closely as possible. The disadvantage of this two-layer strategy lies in the lower layer resisting all the disturbance for satisfactory tracking results, regardless of the fact that part of the disturbance will be beneficial to economic performance. In order to realize the economic promotion in batch processes, a batch-to-batch optimization method is proposed in this paper. As an alternative to the traditional lower-layer tracking MPC, the proposed algorithm uses Gaussian process regression to establish a more accurate linear time-varying model and learns the information from the previous batches to obtain a more precise state estimation, thus continuously moving up economics by solving the economic optimization problem. Convergence of the proposed algorithm is proved and simulation is performed to verify the effectiveness of the proposed algorithm on economic performance improvement in comparison with the traditional tracking one. • An economic performance improvement strategy is proposed for batch processes. • Disturbances are utilized instead of being resisted to promote economy. • Gaussian process regression is utilized to compensate for model-plant mismatch. • Kalman filter is adopted to realize an accurate state prediction of the batches. [ABSTRACT FROM AUTHOR]

• Considering the problems of constraints, nonlinearity and parameter uncertainties, we add a robust iterative learning controller into 2D system. Then integrated control methods are proposed. • The analyses of robust convergence and tracking performance based on 2D system are given in this paper. • The proposed control strategy can effectively improve the robustness of the system. This paper studies the problem of integrated control in the 2-dimensional (2D) system with parameter uncertainties for batch processes. An integrated iterative learning control (ILC) strategy based on quadratic performance for batch processes is proposed. It realizes comprehensive control by combining robust ILC in batch-axis with model predictive control (MPC) in time-axis. The design of quadratic-criterion-based ILC for the system can be converted into a min-max problem. Then a model predictive controller with time-varying prediction horizon is designed based on a quadratic cost function. For an uncertain model, a novel integrated robust ILC scheme based on a nominal model is further proposed. As a result, the control law of the 2D system can be regulated during one batch, which leads to good tracking performance and strong robustness against the disturbance and the uncertainties. Moreover, the analyses of the convergence and tracking performance are given. The proposed methods are applied to batch reactor, and results demonstrate that the system has good robustness and convergence. This paper provides a new way for batch processes control. [ABSTRACT FROM AUTHOR]

This paper presents a novel adaptive resonance theory (ART)-based modular architecture for unsupervised learning, namely the distributed dual vigilance fuzzy ART (DDVFA). DDVFA consists of a global ART system whose nodes are local fuzzy ART modules. It is equipped with distributed higher-order activation and match functions and a dual vigilance mechanism. Together, these allow DDVFA to perform unsupervised modularization, create multi-prototype cluster representations, retrieve arbitrarily-shaped clusters, and reduce category proliferation. Another important contribution is the reduction of order-dependence, an issue that affects any agglomerative clustering method. This paper demonstrates two approaches for mitigating order-dependence: pre-processing using visual assessment of cluster tendency (VAT) or post-processing using a novel Merge ART module. The former is suitable for batch processing, whereas the latter also works for online learning. Experimental results in online mode carried out on 30 benchmark data sets show that DDVFA cascaded with Merge ART statistically outperformed the best other ART-based systems when samples were randomly presented. Conversely, they were found to be statistically equivalent in offline mode when samples were pre-processed using VAT. Remarkably, performance comparisons to non-ART-based clustering algorithms show that DDVFA (which learns incrementally) was also statistically equivalent to the non-incremental (offline) methods of density-based spatial clustering of applications with noise (DBSCAN), single linkage hierarchical agglomerative clustering (SL-HAC), and k-means, while retaining the appealing properties of ART. Links to the source code and data are provided. Considering the algorithm's simplicity, online learning capability, and performance, it is an ideal choice for many agglomerative clustering applications. [ABSTRACT FROM AUTHOR]

This paper develops an indirect iterative learning control scheme for batch processes with time-varying uncertainties, input delay, and disturbances. In this paper, a predictor based on a state observer is designed to estimate the future state and to compensate for the input delay. Then a feedback controller based on the estimated state and the set-point error is used to track the specified reference trajectory, where, of the options available, a robust H ∞ controller is designed in the presence of time-varying uncertainties and load disturbances. Then a proportional plus derivative type iterative learning control law is designed. An injection molding process model demonstrates the new method's effectiveness, and a comparison with a direct-type design is given. • Indirect iterative learning control. • Design based on an observer and the tracking error. • Robust H ∞ performance for nonrepetitive distrubances. [ABSTRACT FROM AUTHOR]

Big Data will revolutionize modern industry by improving process optimization, facilitating insight discovery, and improving decision-making. This big data revolution presents a multitude of possibilities and challenges in evolving from traditional batch processes to smart batch processes. This tremendous potential requires the ability to extract value from vast amounts of industrial process data. Using a new three-dimensional (3D) perspective of time, batch, and operational context, this paper explores smart batch processes with higher efficiency, greater profitability, and longer sustainability. First, we review the traditional one-dimensional (1D) perspective on batch processes and summarize the existing two-dimensional (2D) perspectives on batch processes, i.e., modeling, monitoring, control, and optimization methods. Based on those results, the spotlight will focus on how big data can be used to achieve smart batch processes using the 3D perspective. This will include detailed discussions of definitions and concepts, operational mechanisms, and the benefits and advantages of smart batch processes. For further implementation of the 3D perspective, we present several monitoring and control methodologies. Next, we analyze several challenges and issues in implementing smart batch processes in the era of Big Data. In conclusion, we provide both a novel viewpoint and encouragement for future research into batch process automation from the 3D perspective. [ABSTRACT FROM AUTHOR]

• A meta-learning based functional continuous state space (ML-FCSS) model is built. • The ML-FCSS model uses wavelet function properties to address irregular sampling. • ML-FCSS quickly establishes reliable monitoring for new batches with limited data. • The effectiveness of the ML-FCSS monitoring is demonstrated through comparisons. In an industrial batch, it is common to produce products with different grades, so only a limited number of data are available for each grade of product. The data are also easily contaminated by external disturbances and noise. The sampling interval would be messy and irregular if contaminated samples were removed. Thus, it is difficult to build models for monitoring the corresponding process. This paper proposes a meta-learning-based functional continuous state space model for fast modeling and monitoring of multi-grade batch processes. It takes advantage of wavelet function properties to effectively handle the irregular sampling issue. In addition, the meta-learning method uses diverse historical multi-grade datasets for modeling. It rapidly establishes an efficient and reliable monitoring model for new running batches with a small number of batch data. The effectiveness of the proposed method is shown through a numerical case and an industrial case in the chemical batch process. [ABSTRACT FROM AUTHOR]

• Introduction of a control strategy for batch tablet film-coating processes. • Effective use of available process data for online control of critical variables. • Use of a dynamic process simulator to validate a decentralized PI controller. • Potential reduction in total batch processing time and energy consumption. This paper proposes a control strategy for batch tablet film-coating processes using readily available process instrumentation. Outlet air conditions (temperature and relative humidity), which impact coating quality, are controlled by the manipulation of inlet air temperature and coating spray rate. Controller design, tuning and validation rely on the use of a dynamic process simulator, calibrated using data collected from a pilot plant coating unit. The final 2 × 2 decentralized PI controller demonstrated good performance for both setpoint tracking and disturbance rejection in simulation. Additionally, controlled variable setpoints were determined to maximize plant profits considering batch duration and energy consumption as optimization criteria. For the case study considered, the total batch processing time can potentially be reduced by 40 % while simultaneously reducing energy usage by 30 %, in comparison with the more conservative fixed recipe approach typically adopted in production. [ABSTRACT FROM AUTHOR]

The pivotal factor to regulate and enhance within an operating batch is the quality process, a challenging variable to monitor online. Soft sensors offer an immediate alternative for providing real-time insights into process quality, yet persisting issues include the imbalance between process and quality measurements, noisy measurements, and the intricate 3-dimensional dynamic batch data structure. This paper introduces the Robust Semi-Supervised Dual-Attention Latent Dynamic Conditional State-Space Model (RS2DA-LDCSSM) to address these challenges for within-batch quality prediction. Given the frequent absence of quality data due to measurement inconveniences, an imputation network is embedded within the RS2DA-LDCSSM to facilitate the prediction of future quality. To prevent information distortion during data unfolding, the Attentional Sequence-to-Sequence RNN Encoder-Decoder (AS2S-RNNED) is employed to process the 3-dimensional batch data. The proposed method integrates AS2S-RNNED with a probability state-space model to filter out noisy process data and stabilize the probability prediction of quality data, extracting spatial and temporal latent data from past process and quality data while minimizing the loss in multi-step prediction. This work represents a novel approach to dynamic nonlinear batch processes through probabilistic semi-supervised learning. The RS2DA-LDCSSM is adaptable to any batch process for within-batch quality prediction, as evidenced by numerical cases demonstrating its reliability, achieving an R2 value of 0.87, surpassing comparison methods. In an industrial penicillin fermentation batch process, the RS2DA-LDCSSM exhibits remarkable robustness with a high R2 index value of 0.99 in practical quality prediction scenarios, showcasing its efficacy. • Semi-supervised model training utilizes unlabeled process data. • Separate networks are crafted for process and quality feature extraction. • The state-space model with the encoder-decoder is used to extract latent features. • A multi-step-ahead prediction learning scheme yields favorable prediction results. [ABSTRACT FROM AUTHOR]

This paper proposes an output feedback (OF) based PD-type robust iterative learning control (ILC) scheme for a class of discrete batch processes with time-varying delays and uncertainties in the absence of accurate state measurements, which is convenient for practical applications. Based on a repetitive process model and Lyapunov–Krasovskii theory, sufficient conditions are established in terms of linear matrix inequalities (LMIs) to ensure the robust stability along the trial for all admissible uncertainties. A two-stage heuristic approach is developed to iteratively calculate the feasible ILC gains based on a pre-designed state feedback (SF) controller, which circumvents the non-convex stability conditions expressed in bilinear matrix inequalities caused by OF. In addition, the conditions can be extended to the case of non-repetitive uncertainties in the system and meet the prescribed H ∞ performance level. Finally, a simulation of injection molding process is given to demonstrate the effectiveness of the proposed method. • PD-type iterative learning control (ILC) for batch processes with time-varying delays. • The robust H ∞ performance index is considered under non-repetitive uncertainties. • Robust ILC updating law based on linear repetitive process model setting. • Using output information is beneficial to practical implementation. • The heuristic algorithm circumvents the non-convex stability conditions. [ABSTRACT FROM AUTHOR]

• Two candidate models for the transitional phase monitoring. • A developed index for the selection of the two candidate models. • Two applications validate the reliability of the proposed strategy. In the batch processes, the transitional phase can be regarded as a connection process between its two neighboring stable phases. Thus, the effect on the transitional phase from its former phase to its latter phase exists. In this paper, a new transitional phase modeling and monitoring strategy is proposed. It provides two candidate models with respect to the effect of its two neighboring phases respectively. As a result, two candidate models are established for the transitional phase monitoring. To solve the above problem, an index is developed in this paper. The index is built with the combination of all the statistics and their corresponding control limits. In addition, as a key tool, it helps to select the suitable candidate model for every sample during the transitional phase. The reliability of the proposed strategy is illustrated with both the real three-tank experimental system and the Tennessee Eastman Process. [ABSTRACT FROM AUTHOR]

Abstract Run-to-run (R2R) control is widely used in semiconductor manufacturing systems to minimize the process drift, shift and variability. The R2R controller adjusts control actions or recipes in a supervisory manner after each batch. This paper provides a comprehensive literature review of R2R control methods for the batch process. First, the principles of major R2R controllers are introduced and analyzed, such as exponentially weighted moving average (EWMA), double exponentially weighted moving average (d-EWMA), model predictive control (MPC), optimizing adaptive quality controller (OAQC), artificial neural network (ANN). Besides, simulation examples with different R2R controllers are made to compare the robustness and adaptability. Then, several case studies concerning a chemical mechanical planarization (CMP) process, a multi-input and multi-output (MIMO) system of the furnace process control and the management of blood glucose (BG) are presented. Finally, the paper concludes with some recommendations and directions for the future research. [ABSTRACT FROM AUTHOR]

To scientifically and reasonably set and adjust the machining parameters, and improve the machining benefit of the batch machining process including machining quality, performance and efficiency, this paper proposes a low cost and high accurate multi-objective optimization framework by integrating intelligent technologies from three perspectives of physical experiment design, objective function construction and multi-objective optimization. In this framework, multilayer design (MLD) which is a computer experiment design method with low redundancy, good uniformity and filling is applied to the machining physics experiment design to reduce the experiment cost. Whale optimization algorithm (WOA) optimized backpropagation neural network (BBPN) is utilized to construct the optimization objective functions to improve the accuracy of functions. And Deep Double Q-Network (DDQN) is employed to achieve multi-objective optimization of machining quality, efficiency, and performance. The effectiveness of MLD, WOA-BBPN and DDQN in multi-objective optimization of machining process are verified by comparisons of common modeling and multi-objective optimization algorithms based on the milling data. These techniques provide a good guidance for the selection of machining parameters to improve the machining benefit and demonstrates some advantages. The research of this paper also offers a good example of applying intelligent technology in machining. • Multilayer experimental design (MLD) is utilized to design the physical experiment of the machining process. • WOA-BBPN is used to mine the nonlinear relationship between machining parameters and processing objectives. • The three objective optimization problem in the machining field is solved by a Deep Double Q-Network. [ABSTRACT FROM AUTHOR]

In this paper, a novel identification method of piecewise affine (PWA) model for batch processes based on constrained clustering technique is proposed. In traditional clustering-based identification approaches, data classification and region partition are performed individually so that inseparable problem usually occurs in the partition phase. The proposed method uses a constrained K-means clustering algorithm to simultaneously perform both data classification and region partition, which is accomplished by imposing the complete and non-overlapping partition constraints into the clustering optimization problem. We employ a greedy iterative approach combined with the golden section search to efficiently solve the constrained clustering problem. This method can greatly improve the accuracy of the identified PWA model. Finally, we demonstrate the effectiveness of the proposed identification method. ● An identification method of PWA model for batch processes based on constrained clustering technique is proposed. ● It uses constrained K-means clustering algorithm to simultaneously perform both data classification and region partition. ● We employ a greedy iterative approach combined with the golden section search to solve the constrained clustering problem. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel framework for hybrid batch-to-batch and within-batch self-optimizing control (SOC) of batch processes. In this framework, batch-to-batch and within-batch controlled variables (CVs) are selected respectively corresponding to the point-wise and profile necessary conditions of optimality of batch processes. For the within-batch SOC, the extended combination matrix is introduced, which is structurally constrained to deal with the causality issue, as well as practical ways for within-batch implementations. The setpoint strategy for a within-batch SOC scheme is also discussed. For the batch-to-batch SOC, which has no causality requirement, the structure of the combination matrix is allowed to be full. The overall combination matrix contains two types of self-optimizing CVs, which will be solved simultaneously in a unified framework. In addition, the active-set change problem is handled using a conservative approach. The hybrid SOC problem is finally characterized as a constrained nonlinear programming (NLP) problem, where the overall combination matrix has particular structural constraints. The design approach is applied to two batch examples, where various benefits and properties of the hybrid SOC methodology are illustrated. The importance of using a simple within-batch SOC scheme is also highlighted, in which case the overall combination matrix is sparse. • The self-optimizing control is extended to a class of constrained batch processes. • A hybrid within-batch and batch-to-batch self-optimizing control strategy is proposed. • The framework is characterized as a structure-constrained optimization problem. • Active-set change is handled by a conservative approach. • Two batch examples are studied to show various advantages of the hybrid approach. [ABSTRACT FROM AUTHOR]

This paper studies a problem of determining the state of a system through costly tests of its components, where components can be tested simultaneously in batches to exploit economies of scale. This problem is a generalization of the classical sequential testing problem and it has applications in various settings, including machine maintenance, disease diagnosis, and new product development. We prove that the problem is strongly NP-hard, model it as a mixed-integer programming formulation, and we also propose a dynamic program for it. Additionally, we design a tabu search and a hybrid solution method that combines a tabu search metaheuristic and a proximity search matheuristic. Based on extensive computational experiments, we find that the dynamic program can solve instances with up to 25 components within a 15-minute time limit and 16 GB of RAM. With respect to larger instances, the proposed metaheuristic and hybrid method are better than a greedy heuristic. • We study sequential testing in batches with resource constraints. • We consider a fixed cost per batch, variable testing costs, and independent prior probabilities for each component. • We propose a mixed-integer programming formulation and a dynamic program. • We develop a tabu search and a hybrid method that combines tabu search with proximity search. [ABSTRACT FROM AUTHOR]

An on-line scheduling problem on m uniform parallel-batch machines to minimize the makespan is considered in this paper. Each machine can process infinite jobs as a batch at a time. Jobs arrive over time and have equal lengths denoted by 1. Compared to parallel machines, uniform machines are more difficult to deal with because they have different processing speeds. There are two types of machines in our model. The lower bound on the competitive ratio is characterized by some complicated algebraic equations. And a best possible on-line algorithm matching the lower bound is constructed and proved in this paper. [ABSTRACT FROM AUTHOR]

Online system identification provides a powerful tool to process control engineers for controller synthesis, process dynamics monitoring, real-time optimization and other purposes at a low computational cost. Instead of processing data in time, this paper intends to propose processing data in a different “direction” – in iteration/batch to improve the estimates tracking performance. However, directly changing the data processing direction gives rise to severe fluctuations on parameter estimates within a batch. To overcome this problem, two online identification methods with simple implementations are devised based on average. One method is applying average in the dual space, while the other in the primal space. The convergence of both approaches has been analyzed. An adaptive average strategy based on moving-window is also developed to track inter-batch dynamics drift. Finally, the simulation results on injection molding, two-tank system and continuous stirred tank reactor (CSTR) testify the improved performance of the methods proposed in this paper. [ABSTRACT FROM AUTHOR]

All-inorganic CsPbX 3 quantum dots (X = Cl, Br, or I) are regarded as a new generation of efficient luminescent materials for displays and solid-state lighting due to their excellent optoelectronic properties. However, the poor stability of CsPbX 3 quantum dots severely hampers their commercial applications. Currently, the in-situ growth of CsPbX 3 quantum dots in amorphous glass has emerged as a crucial approach to significantly enhance their stability. This study successfully synthesized large-scale CsPbBr 3 @glass materials using a one-step self-crystallization method involving melting, quenching, and heat treatment. CsPbBr 3 @glass was blended with polystyrene (PS) through melt blending and extrusion, resulting in the fabrication of a CsPbBr 3 @glass@PS film. By controlling the content of quantum dot glass in polystyrene and adjusting the film's thickness, a green light film with color coordinates (0.1629, 0.7759) and a Full Width Half Maximum (FWHM) of 19.78 nm was achieved. The film produced through this process demonstrated exceptional stability, retaining 90% of its luminous intensity after 1000 h of rigorous aging experiments. Furthermore, when combined with potassium fluorosilicate composite polystyrene film and a blue light chip, a 7-inch LCD display with a color gamut of 135.8% of NTSC 1953 and 92.8% of Rec. 2020 was successfully assembled. This research holds significant reference value for the commercial application of quantum dot glass. Our paper mainly includes the following points: • For the first time, this article tried to sinter large-scale glass powder at one time, and compared the performance of quantum dot glass with different sintering scales, and its performance remained basically unchanged. • By employing a blending and extrusion process, composite plastic particles comprising quantum dot glass and polystyrene were successfully fabricated, demonstrating their suitability for rapid and efficient industrial production. • The optical properties of the film, including emission wavelength range, full width at half maximum, and color coordinates, were effectively adjusted by manipulating the film's thickness and quantum dot glass concentration. • The CsPbBr 3 @glass@PS backlight display film, prepared through the proposed methodology, exhibited sustained and excellent luminescence performance even after undergoing an extensive aging test, validating its long-term stability. • In the assembly of a 7-inch display, the resulting color gamut surpassed the NTSC1953 standard by reaching 135.8%, while achieving a remarkable coverage of the Rec.2020 color space at an impressive level of 92.8%. [ABSTRACT FROM AUTHOR]

Cross-modal hashing has received growing interest to facilitating efficient retrieval across large-scale multi-modal data, and existing methods still face three challenges: 1) Most offline learning works are unsuitable for processing and training with streaming multi-modal data. 2) Current online learning methods rarely consider the potential interdependency between the label categories. 3) Existing supervised methods often utilize pairwise label similarities or adopt relaxation scheme to learn hash codes, which, respectively, require much computation time or accumulate large quantization loss during the learning process. To alleviate these challenges, this paper presents an efficient Online Label Consistent Hashing (OLCH) approach for streaming cross-modal retrieval. The proposed approach first exploits the relative similarity of semantic labels and utilizes the multi-class classification to derive the common semantic vector. Then, an online semantic representation learning framework is adaptively designed to preserve the semantic similarity across different modalities, and a mini-batch online gradient descent approach associated with forward–backward splitting is developed to discriminatively optimize the hash functions. Accordingly, the hash codes are incrementally learned with high discriminative capability, while avoiding high computation complexity to process the streaming data. Extensive experiments highlight the superiority of the proposed approach and show its very competitive performance in comparison with the state-of-the-arts. • Present an efficient online label consistent hashing approach to benefit cross-modal retrieval for streaming data. • Develop an online semantic representation learning framework to preserve the semantic similarity. • Propose a mini-batch online gradient descent approach to incrementally optimize the hash functions. • Exploit a forward–backward splitting scheme to learn the discriminative hash codes. [ABSTRACT FROM AUTHOR]

Efficient attribute reduction algorithm capable of handling high dimensional big data is one of the hot topics of rough set theory, and some related researchers have achieved with |C| jobs. In this paper, we present the definition of a marked reduction set and propose a more efficient attribute reduction algorithm (RA-MRS). The RA-MRS includes a batch processing phase and a vibration optimization phase, which reduce the number of jobs from |C| to log 2 |C|. Additionally, we provide an effective judgment strategy based on MapReduce, which supports the exception processing mechanism of Java to interrupt and advance the current job. Finally, the proposed algorithm is implemented in parallel based on Spark computing framework. The experimental results show that the proposed RA-MRS algorithm is over 99% faster than the classical PAAR_PR algorithm and 70% faster than the algorithm in the literature (Yin et al., 2021). [ABSTRACT FROM AUTHOR]

The development of novel biobased chemicals is of great research significance in promoting the high-value utilization of lignocellulosic biomass. Methyl levulinate glycerol ketal (MLK) as a novel biobased chemical can be used as a solvent and applied in the fields of polyurethane and coatings. In this paper, the process of continuous preparation of MLK from methyl levulinate (ML) and glycerol (GLY) was studied. The effects of different factors on the yield of MLK and the conversion of GLY were investigated, and the optimum reaction conditions were reaction temperature 140 °C, catalyst HZSM-5 0.50 g, ML to GLY molar ratio 4:1 and weight hourly space velocity (WHSV) of 1.34 h−1. Under these conditions, the conversion of GLY was 97.00% and the yield of MLK reached 94.70%. The kinetic experiments were further carried out at the temperature range of 130 °C to 150 °C, and the kinetic results showed that the activation energy and the pre-exponential factor were 64.26 kJ·mol−1 and 9.05 × 109 mol·g−1·s−1, respectively. Meanwhile, the production efficiency in the continuous process was 1.91 g·h−1 with a turnover frequency (TOF) of 9.00 h−1, suggesting a higher catalytic efficiency and catalytic activity than that in the batch process. On this basis, a process simulation of the continuous MLK preparation process was further conducted, and the techno-economic evaluation of the process was performed to provide a theoretical reference for the development of the biobased MLK process. [Display omitted] • An efficient continuous synthesis of biobased MLK was first developed. • The kinetic studies of two processes for MLK synthesis were compared. • The efficiency of the continuous process was more than twice that of the batch process. • A techno-economic analysis of the continuous production process was evaluated. [ABSTRACT FROM AUTHOR]

Water is an essential commodity for society, and alternate resources such as seawater and wastewater are vital for the future. There are various desalination technologies that can provide sufficient and sustainable water sources. Renewable energy-based desalination technologies like solar-based interfacial evaporation are very efficient and sustainable desalination methods. Solar-based interfacial evaporation has been a focus due to its efficient and easy-to-use methods. Still, research is needed for fouling resistance, scalable and low-cost materials, and devices for solar interfacial evaporation. Recent research focuses on the materials for evaporation devices, but various other aspects of device design and fabrication methods are also necessary to improve device performance. In this article, all the evaporator device configurations and strategies for efficient evaporator devices are compiled and summarized. The evaporator devices have been classified into eight main categories: monolayer, bilayer, tree-like design, low-temperature designs, 3D-Origami-based designs, latent heat recovery design, design with storage/batch process, and contactless design. It was found that a good absorber, well-engineered air-water interface, and bottom-layer insulation are necessary for the best systems. The current research focuses on the vapor production output of the devices but not on the water production from devices. So, the focus on device-based water production and the associated cost of the water produced is essential. This article articulates the strategies and various scalable and efficient devices for evaporation-based solar-driven desalination. This article will be helpful for the researchers in improving devices output and coming up with a sustainable desalination and water treatment. [Display omitted] • The paper discusses the outlook and challenges for Solar driven interfacial evaporation devices. • The article lays the path for efficiency improvement for interfacial evaporation Devices. • Floatable devices with tree-like designs have better performance. • The absorber and condenser designs are equally essential for ISDE water production devices. [ABSTRACT FROM AUTHOR]

A wide range of approaches for batch processes monitoring can be found in the literature. This kind of process generates a very peculiar data structure, in which successive measurements of many process variables in each batch run are available. Traditional approaches do not take into account the time series nature of the data. The main reason is that the time series inference theory is not based on replications of time series, as it is in batch process data. Time series models are able to capture the variability only in a time domain (modeling the data serial correlation/data dynamics), but is not aimed to consider the variability between samples of time series. This fact demands some adaptations of this theory to accommodate the model coefficient estimates from a group of batches, considering jointly the batch to batch samples variability (batch domain) and the serial correlation in each batch (time domain). In order to address this issue, this paper proposes a new approach grounded in a group of control charts based on the classical ARMA model for monitoring and diagnostic of batch processes. The model coefficients are estimated for each historical time series sample batch and modified Hotelling and t-Student statistics are proposed to accommodate those estimates. A group of control charts based on that statistics are proposed for monitoring the new batches. Additionally, those groups of charts can help the fault diagnosis, identifying the source of disturbances. Through simulated and real data we show that this approach seems to work well in both purposes, compared with the two traditional competitors. • A new control approach is proposed for batch processes. • A group of ARMA-based control charts is presented for fault detection and diagnosis. • The charts are based on the ARMA coefficient estimates from reference batches. • These charts consider the data dynamics and the batch-to-batch variability. • The charts power is illustrated by its application on simulated and real data. [ABSTRACT FROM AUTHOR]

In this paper, we express the Chord Length Distribution (CLD) measure associated to a given Particle Size Distribution (PSD) when particles are modeled as suspended spheroids in a reactor. Using this approach, we propose two methods to reconstruct the unknown PSD from its CLD. In the single-shape case where all spheroids have the same shape, a Tikhonov regularization procedure is implemented. In the multi-shape case, the measured CLD mixes the contribution of the PSD associated to each shape. Then, an evolution model for a batch crystallization process allows to introduce a Back and Forth Nudging (BFN) algorithm, based on dynamical observers. We prove the convergence of this method when crystals are split into two clusters: spheres and elongated spheroids. These methods are illustrated with numerical simulations. [ABSTRACT FROM AUTHOR]

Increasing the productivity of batch processes presents a complex challenge, with difficulties resulting from, amongst other things, the time-varying and non-linear characteristics that such processes exhibit. This paper presents an innovative optimisation technique which utilises a data-driven Gaussian process regression model, built on time-varying and non-linear historical process data, to iteratively increase batch productivity from one cycle to the next. Specifically, productivity is increased by making appropriate adjustments to batch cycle time and the trajectory of a manipulated variable. The capabilities of the proposed method are demonstrated using two benchmark fermentation simulations, Penicillin production (Pensim) and Saccharomyces Cerevisiae , where it is shown to achieve increases in productivity of between 60% and 97% compared with what was achieved using 'golden batch' conditions. • GPR outperforms PLS in process modelling extrapolation. • Data driven models used to optimise productivity of batch process. • Performance of iterative optimisation dependent on tuning factor. [ABSTRACT FROM AUTHOR]

This paper presents an eco-friendly approach for minimizing heavy metal pollution, offering food waste valorization and energy source at the same time. It focuses on Cu(II) sorption by low cost sorbent developed by mechanical treatment of Prunus persica L. stones, a food industry waste biomass. Removal of Cu(II) was studied at different operating parameters in a batch sorption system, with special attention on temperature effect on sorption process. The pseudo-first order, pseudo-second order and Elovich model were applied to test kinetic experimental data. Equilibrium experimental results were fitted to Langmuir, Freundlich, Sips, Toth and Dubinin–Radushkevich isotherms to elucidate the type and sorption performance. Characteristic functional groups responsible for Cu(II) binding and thermal behavior of PS have been investigated using FT-IR and TGA analyses. The activation parameters were calculated using Arrhenius and Eyring equations. Using equilibrium data at different temperatures, thermodynamic parameters ΔG 0 , ΔH 0 and ΔS 0 were calculated, suggesting that the sorption of Cu(II) on PS is spontaneous and endothermic process with increased randomness during the sorption. The isosteric heat of adsorption ( Δ iso H ) was determined, suggesting the non-uniformity of PS surface and lateral interactions among ions sorbed. Desorption experiments performed with different chemicals have confirmed the reusability of PS for five cycles, without losing and even improving its sorption capacity. Results presented in this paper might help in appropriate design of purification systems using this type of lignocellulosic waste. [ABSTRACT FROM AUTHOR]

In order to ensure the consistency of the fabricated devices in batches, calibration is required on CMOS temperature sensor before delivery to compensate the tracking errors of the devices. In the paper, a wafer-level three-step calibration technique for BJT-based CMOS temperature sensor is proposed and fabricated based on TSMC 180-nm-BCD technology. The calibration approach is based on the wafer level, with the simplified calibration process. The strategy of the fuse burning directly maps with the proposed three-step calibration approach. The technique shows encouraging results in removing the error band of the sensor response caused by processing variations in batch fabrication. The calibrated sensor can realize an inaccuracy (±3δ) within ±0.3 °C from −40 °C to 125 °C. The temperature sensor with the calibration module in the paper has great potential application in mobile phones, battery management, disk drives and other fields, etc.. • The technique can effectively reduce the error caused by process variations. • The strategy of the fuse burning directly maps with the calibration approach. • The chip is calibrated during the wafer level test based on probe station. • The technique can realize an inaccuracy (±3δ) within ±0.3 °C from −40 °C to 125 °C. [ABSTRACT FROM AUTHOR]