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The laser powder bed fusion (L-PBF) additive manufacturing process offers many advantages. However, its industrial integration still needs to be improved due to the certification constraints of the produced parts. Thus, this paper proposes a phenomenological causal chain of process failures to ensure the control of the L-PBF process and further certify the quality of the parts produced. Consequences of unsuitable thermal regimes are analysed using a failure mode and effects analysis (FMEA) approach, allowing a more global understanding of all the causal chain-related data. The proposed approach is based on standard acquisition equipment available on industrial machines and proves relevant for identifying process failures. Experimental validation of the proposed monitoring method is implemented on a use case using dedicated software. Finally, it is shown that the method is relevant in this confrontation and compatible with the time scale of the process for future NC integration and real-time monitoring. [ABSTRACT FROM AUTHOR]

Nowadays many processes in the food industry are monitored in an automatic way, with the purpose of minimising the need for workforce and of ensuring the proper control of the quality and safety of the foodstuff. All the sensors share data with a centralised management unit, where often a Manufacturing Execution System collects and evaluates them. As reported in recent research, however, a further step that can be undertaken, exploiting Industry 4.0 enabling technologies, is the implementation of digital twin approaches, with the additional aim to prevent possible issues during production. In line with these considerations, this work aims at showing two different digital twin models intended for improving the control of as many real food systems. Liquid and powder fluids are taken as examples for highlighting the differences in the optimization of the two food processes, as well as for fully exploring the potential of the digital twin approach. Finally, based on the real data taken from two pilot plants, a framework for the selection of the best digital twin tool in the food sector is delineated. [ABSTRACT FROM AUTHOR]

The use of holding and inspection fixtures is common across many sectors of manufacturing. The concept of a fixture for holding a component, for the purposes of assembly or inspection, is straightforward. The fixture design and the associated clamping strategy can have a significant impact on the process. This paper presents a methodology for investigating the effects of clamping sequence for a production inspection fixture, on the dimensional variability of an automotive production-representative sheet metal sub-assembly, along with experimental findings and analysis of measurement data. The study utilises both a coordinate measuring machine and laser tracker to capture a range of features and surface points, and compares four different clamping sequences, including the manufacturer's defined sequence, to evaluate their effect on the dimensional results from a predetermined measurement plan. The results from the study show that there was significant variation in measurements taken from the four different clamping sequences, and these variations can show the same points and/or features being within tolerance for one clamping sequence and out of tolerance for another. This clearly has major implications for product development and subsequent volume manufacture, so needs to be considered and optimised in the measurement planning process. [ABSTRACT FROM AUTHOR]

Process control is crucial in the operations of chemical engineering plants and manufacturing processes across a broad range of industries. This report details the testing and optimization of P, PI, PID controllers for a temperature control system. The first objective is to determine the best controller type for controlling the system temperature. The second objective is to alter tuning parameters manually for PID controllers in the temperature control system to achieve optimality. The performance criteria include the settling time, peak-to-trough ratio, and steady state offset. It is discovered that PID controller is the best due to its fast response,offset elimination, and minimized settling time and oscillations, combining the aspects of Proportional (P), Integral (I), and Derivative (D) control. Additionally, manual tuning attempts were made to fine-tune the PID controller. However, the lack of data points lead to inconclusive results. Future works should conduct additional trials or employ alternative tuning methods.

A custom‐made Raman setup is improved and adjusted to allow its use in Ex zones by using a 1‐mW laser and no optical focusing elements. The application under consideration is the hydroformylation of decene to undecanal, which must take place under explosion protection. To investigate the dispersive mixture, a separation takes place in a phase separation cell in which the Raman probe performs measurements. It can be shown that, with the use of highly sensitive detectors, even very low excitation powers can lead to a processable signal, and thus the application of Raman measurement technology far below the requirements of explosion protection is possible. [ABSTRACT FROM AUTHOR]

Operators in modular process plants face new demands due to features like increasing automation and digitalization combined with an architecture allowing for reconfigurations in short time spans. However, operators must be able to master process control tasks at all times. For research purposes on topics like supporting operators in their performance in process control of modular process plants, this article aims at developing scenarios of such plants. Based on three conceptual levels describing the relation of chemical processes and their modularized realization and a neutralization process as use case, four scenarios were developed. Having the common task to increase the throughput, the scenarios differ in terms of complexity and constraints that need to be considered for process control. [ABSTRACT FROM AUTHOR]

In this paper, the problem of monitoring image processes with spatially correlated pixels over time is considered. An exponentially weighted moving average (EWMA) control chart for monitoring such processes based on a convolutional neural network (CNN) is proposed. A comparison of its performance with a Hotelling's T2$$ {T}^2 $$ control chart and with a control chart based on generalized likelihood ratio (GLR) approach is conducted through a simulation study. The new method outperforms other methods in most of the cases considered in the simulation study. A technique for mean intensity shift localization based on CNNs is proposed and evaluated. [ABSTRACT FROM AUTHOR]

AbstractThis study explores controlling a first- and second-generation alcoholic fermentation process with cell recycling, modeled through algebraic differential equations (DAE) with constraints. It evaluates seven controller types: PI, PID, Model Predictive Control (MPC), Neural Network Model Predictive Control (NNMPC), Mixed Model Predictive Control (MMPC), Internal Model Control (IMC), and Linear Quadratic Regulator (LQR). Results show that, while PI and PID controllers can track setpoints, they exhibit slow responses and oscillations. In contrast, MPC controllers respond faster, with both MPC and MMPC demonstrating more robust dynamics, achieving a significant reduction in Integral of the Absolute Error (IAE) across various disturbances, notably an 87% reduction in regulatory scenarios. NNMPC outperforms PI and PID but exhibits overshoot and oscillations, and lacks robustness for servo-type problems. However, MMPC showcases comparable or superior performance to MPC, surpassing other controllers in robustness, especially the IMC and LQR in the regulatory problems. NNMPC maintains a simulation time of under 4 seconds, whereas MPC incurs a computational cost 1,000 times higher. Integrating NNMPC’s optimal increment as an initial estimate in MPC reduces computational time by up to 79.7%. These findings highlight the ANN’s effectiveness in addressing complex control challenges, especially when integrated with MPC. MMPC offers a superior balance between accuracy, robustness, and computational efficiency, serving as a promising solution for reducing computational costs in MPC-type controllers. [ABSTRACT FROM AUTHOR]

Statistical Process Control (SPC) is employed to support the effective quality performance of production and manufacturing processes. The control chart is a type of SPC utilized to reduce variability in key quality parameters, leading to the production of quality conforming products. Control charts, such as the Exponentially Weighted Moving Average (EWMA) are widely used to reduce variation, improve productivity, and ensure quality. However, small manufacturers face challenges that may prevent their effective use of quality control charts. One such challenge relates to resource constraints. These constraints tend to inhibit the conduct of quality inspections at high rates. With this in mind, the present study sets out to develop and present an improved EWMA control chart for effective quality control that takes into consideration significant resource constraints in a small aluminium manufacturing firm. The findings suggest that adopting the improved EWMA chart can enhance the detection of any increase in defectives without the need for additional resources. The improved EWMA control chart provides small manufacturers with an enhanced quality conforming tool developed based on practical realities. Implications for both practice and theory are also highlighted. [ABSTRACT FROM AUTHOR]

AbstractE-commerce provides a vast space for user-generated content, including user reviews from the initial use of products or services to subsequent usage. Existing methods mainly focus on unmodified reviews and ignore customer perception after more experience with products. To address this, we propose a Markov chain-based bi-channel dynamic topic model (BDTM), which extends the sequential structure of the dynamic topic model and incorporates initial customer reviews and additional reviews to reflect topic shifts caused by customer experience perception. Then, a control chart based on word mover’s distance (WMD), called a BDTM-WMD (B-W) chart, is proposed to monitor topic shifts under BDTM. An alternative multiscale dynamic topic model (M-K) chart is constructed for comparison. Using a simulation approach, we find that compared with the existing sequential reverse joint sentiment-topic (SRJST) chart and joint sentiment topic-rating meets review (JSTRMR) chart, the proposed B-W chart is more sensitive to small shifts. Case study 1, using real data, shows the advantages of the proposed BDTM, B-W chart and M-K chart under both initial and additional reviews. Case study 2 shows that with additional reviews, the proposed B-W chart triggers out-of-control signals earlier than those by the existing joint sentiment topic-rating meets review (JSTRMR) chart. [ABSTRACT FROM AUTHOR]

A large amount of agricultural waste has been produced in the process of production and living. The livestock and poultry manure and crop straw have an annual output of about 3.05, and 870 million tons, respectively. Agricultural waste has enormous biological potential, referred to as a "misplaced resource". It is in high demand for an important measure to promote the utilization of agricultural waste resources for the prevention and control of non-point source pollution under the living environment in rural revitalization. A series of regulations and standards have been introduced to fully utilize straw, livestock, and poultry manure in recent years. Planting and raising are combined into a cycle from "treatment" to "reuse". However, China's agricultural waste reuse has just started, compared with the developed countries. Crop straws and domestic manure are either returned to the field or used as low-value-added feed and fertilizer in China, whereas the high-value-added use has not taken shape yet. Existing policies and regulations cannot fully meet the demand at present, due to mismatched planting and animal raising, under-developed technology, and equipment for manure reuse. It is urgent to require innovative technology, in order to reduce the severe nutrient loss of organic fertilizer and insufficient use of remaining nutrients in the field. It is still lacking research on the treatment of agricultural waste resources. The unreasonable disposal of agricultural waste has caused serious environmental pollution and a large amount of resource waste. The treatment of agricultural waste has been confined to better accelerate modern agriculture in rural areas. As important agricultural countries in the world, Australia and New Zealand have established relatively mature recycling systems for agricultural waste. This study aims to sort out the related technology systems in Australia, New Zealand, and Singapore using field research. A summary was formed from their typical experiences and practices in innovation, platform building, talent training, and application. A strong reference was then provided for the highly efficient reuse of waste in China. As such, the pollutant emission was reduced to improve the mechanization concerning waste reuse. Australia formed a technology system of "Resource +"; New Zealand was a synergistic treatment system of "Waste Source Reduction + Process Control"; and Singapore shared a new anaerobic fermentation for turning municipal domestic waste into resources. The key technologies were reviewed for the waste reused as a resource, equipment for manure returning to the field, and local treatment of manure for small and medium-sized animal-raising farms. The innovation platform was improved to establish a host of world-class laboratories for green agriculture. The manure returning to the field was enhanced to combine the planting and animal raising, in order to promote the integrated application in green agriculture. [ABSTRACT FROM AUTHOR]

Corn syrup is a cost-effective sweetener ingredient for the food industry. In producing syrup from corn, process control to enhance and/or maintain a constant dextrose equivalent value (DE) is a constant challenge, especially in semi-automated/batch production settings, which are common in small to medium-size factories. Existing work has focused on continuous process control to keep parameter values within a setpoint. The machine learning method applied is for time series data. This study focuses on building process control models to enable semi-automation in small to medium-size factories in which the data are not as time dependent. Correlation coefficients were used to identify key process parameters that contribute to feed pH value and DE. Artificial neural network (ANN), support vector machine (SVM), and linear regression (LR) models were built to predict feed pH and DE. The results suggest (1) model accuracy ranges from 91% to 96%; (2) the ANN models yielded about 1% to 3% higher accuracy than the SVM and LR models and the prediction accuracy is robust even with as few as six data sets; (3) both the SVM and ANN models have noise tolerant properties, but ANN has a higher noise tolerance than SVM; (4) SVM performance can be hindered when using high-dimensional data sets; (5) the LR model yields higher variation in accuracy prediction than ANN and SVM; (6) distribution fitting is a good approach for generating data; however, fidelity of fitting can greatly impact accuracy; and (7) multi-stage models yield higher accuracy than single-stage models, but there are pros and cons to each approach. [ABSTRACT FROM AUTHOR]

Statistically based experimental designs have been available for over a century. However, many preclinical researchers are completely unaware of these methods, and the success of experiments is usually equated only with ' p < 0.05'. By contrast, a well-thought-out experimental design strategy provides data with evidentiary and scientific value. A value-based strategy requires implementation of statistical design principles coupled with basic project management techniques. This article outlines the three phases of a value-based design strategy: proper framing of the research question, statistically based operationalisation through careful selection and structuring of appropriate inputs, and incorporation of methods that minimise bias and process variation. Appropriate study design increases study validity and the evidentiary strength of the results, reduces animal numbers, and reduces waste from noninformative experiments. Statistically based experimental design is thus a key component of the 'Reduction' pillar of the 3R (Replacement, Reduction, Refinement) principles for ethical animal research. [ABSTRACT FROM AUTHOR]

In this paper, an interval type-2 radial basis function neural network system is presented that produces an accurate forecast of the temperature of liquid steel in a secondary metallurgical monitoring and control process. The main goal of this proposal is to get the precise temperatures and times to aggregate the additives to the liquid bath to produce high-strength, low-alloy steel and not waste materials due to the oxidation caused by high temperatures. Also, the proposal reduces the replacement times of measurement instruments that are damaged by the high temperatures. The novelty of this proposal is the lack of this class of neural networks in steelmaking processes and the fact that this is an emergent technology that has only been around for less than 10 years. The produced results show an error in order of 0.12% that is a figure well below 0.30% which is the typo error produced by the measurement devices such as thermocouples of the k-type that are damaged at 1600° and produce erroneous measurements in temperatures superior to 1200 °C and less than the classic interval type-2 fuzzy logic systems. [ABSTRACT FROM AUTHOR]

Aerosol Jet Printing (AJP) is an additive manufacturing process that deposits ink-like materials suspended as an aerosol mist. AJP creates three-dimensional (3D) functional structures onto flat or conformal surfaces in complex shapes without the aid of additional tooling, enabling the manufacturing of extremely fine electrical interconnects with freeform structures. Due to the novelty and complexity of AJP, physical understanding is rather limited, hindering physics-based process modeling and analysis. Fortunately, the data resources from AJP applications, e.g., 3D Computer-Aided-Design data, Standard Triangle Language files, in-situ images of part, and nozzle motion records, provide an unparalleled opportunity for developing data-driven, Machine Learning (ML) methods to characterize AJP processes, support process control, and facilitate product improvement. To thoroughly identify the newfound opportunities, this study reviews state-of-the-art ML methods used in AJP applications, investigates open issues in AJP, and outlooks future development of ML-based research topics for AJP. It sheds light on how to maximize the value of ML on AJP data to develop scalable, generalizable decision-making methods. More future works along the direction will be motivated. [ABSTRACT FROM AUTHOR]

As a consequence of the increasing market share of hybrid and all-electric mobility solutions, there is a need for robust manufacturing technologies that enable the efficient production of powerful electric traction motors in automotive industry. In the context of high-quality distributed stator windings, the so-called hairpin technology already meets the automotive demand regarding productivity – but process reliability is still limited by variations in the mechanical and geometric properties of the enameled rectangular copper wire leading to different springback effects within the forming processes. Against this background, a model-based closed-loop process control for the sequential tool-bound bending of hairpin coils is introduced and validated in this paper. [ABSTRACT FROM AUTHOR]

This work presents a framework for interfacing a reinforcement learning algorithm with a finite element model in order to develop an artificial neural network controller. The goal of the controller is accelerating the hot compression process of the titanium aluminide TNM-B1. The reinforcement learning algorithm interacts with the finite element model by exploring different die velocities and receiving input measurements (the velocity, displacement and force of the die) while collecting rewards if a constant stress state in the workpiece is achieved. Synthetic stochastic material behavior was used to simulate the observed variations in deformation behavior of TNM-B1. The same reinforcement learning setup and reward function was able to adapt to two example finite element environments; the compression of a simple cylinder workpiece between flat dies and the compression of a more complex bone workpiece between flat dies. The performance of the controller for the bone compression environment was comparatively reduced and less consistent. In addition, training times and training instability were significantly increased. Furthermore, the results suggest that the framework can be used as a tool to find process optimizations or alternative process routes. This work demonstrates the concept and provides the groundwork and fundamentals for transferring the method to a physical setup. [ABSTRACT FROM AUTHOR]

Ethanol production is a significant industrial bioprocess for energy. The primary objective of this study is to control the process reactor temperature to get the desired product, that is, ethanol. Advanced model‐based control systems face challenges due to model‐process mismatch, but Reinforcement Learning (RL) is a class of machine learning which can help by allowing agents to learn policies directly from the environment. Hence a RL algorithm called twin delayed deep deterministic policy gradient (TD3) is employed. The control of reactor temperature is categorized into two categories namely unconstrained and constrained control approaches. The TD3 with various reward functions are tested on a nonlinear bioreactor model. The results are compared with existing popular RL algorithm, namely, deep deterministic policy gradient (DDPG) algorithm with a performance measure such as mean squared error (MSE). In the unconstrained control of the bioreactor, the TD3 based controller designed with the integral absolute error (IAE) reward yields a lower MSE of 0.22, whereas the DDPG produces an MSE of 0.29. Similarly, in the case of constrained controller, TD3 based controller designed with the IAE reward yields a lower MSE of 0.38, whereas DDPG produces an MSE of 0.48. In addition, the TD3 trained agent successfully rejects the disturbances, namely, input flow rate and inlet temperature in addition to a setpoint change with better performance metrics. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter 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.)

Emerging technological advances are reshaping the casting sector in latest decades. Casting technology is evolving towards intelligent casting paradigm that involves automation, greenization and intelligentization, which attracts more and more attention from the academic and industry communities. In this paper, the main features of casting technology were briefly summarized and forecasted, and the recent developments of key technologies and the innovative efforts made in promoting intelligent casting process were discussed. Moreover, the technical visions of intelligent casting process were also put forward. The key technologies for intelligent casting process comprise 3D printing technologies, intelligent mold technologies and intelligent process control technologies. In future, the intelligent mold that derived from mold with sensors, control devices and actuators will probably incorporate the Internet of Things, online inspection, embedded simulation, decision-making and control system, and other technologies to form intelligent cyber-physical casting system, which may pave the way to realize intelligent casting. It is promising that the intelligent casting process will eventually achieve the goal of real-time process optimization and full-scale control, with the defects, microstructure, performance, and service life of the fabricated castings can be accurately predicted and tailored. [ABSTRACT FROM AUTHOR]

The control of the injection speed in hydraulic injection molding machines is critical to product quality and production efficiency. This paper analyzes servomotor-driven constant pump hydraulic systems in injection molding machines to achieve optimal tracking control of the injection speed. We propose an efficient reinforcement learning (RL)-based approach to achieve fast tracking control of the injection speed within predefined time constraints. First, we construct a precise Markov decision process model that defines the state space, action space, and reward function. Then, we establish a tracking strategy using the Deep Deterministic Policy Gradient RL method, which allows the controller to learn optimal policies by interacting with the environment. Careful attention is also paid to the network architecture and the definition of states/actions to ensure the effectiveness of the proposed method. Extensive numerical results validate the proposed approach and demonstrate accurate and efficient tracking of the injection velocity. The controller's ability to learn and adapt in real time provides a significant advantage over the traditional Proportion Integration Differentiation controller. The proposed method provides a practical solution to the challenge of maintaining accurate control of the injection speed in the manufacturing process. [ABSTRACT FROM AUTHOR]

In the process of laser-based direct energy deposition (DED-LB), the quality of the deposited layer will be affected by the process parameters and the external environment, and there are problems such as poor stability and low accuracy. A molten pool monitoring method based on coaxial vision is proposed. Firstly, the molten pool image is captured by a coaxial CCD camera, and the geometric features of the molten pool are accurately extracted by image processing techniques such as grayscale, median filtering noise reduction, and K-means clustering combined with threshold segmentation. The molten pool width is accurately extracted by the Canny operator combined with the minimum boundary rectangle method, and it is used as the feedback of weld pool control. The influence of process parameters on the molten pool was further analyzed. The results show that with an increase in laser power, the width and area of the molten pool increase monotonously, but exceeding the material limit will cause distortion. Increasing the scanning speed will reduce the size of the molten pool. By comparing the molten pool under constant power mode and width control mode, it is found that in width control mode, the melt pool width fluctuates less, and the machining accuracy is improved, validating the effectiveness of the real-time control system. [ABSTRACT FROM AUTHOR]

Copyright of Metal Working (1674-165X) is the property of Metal Working Editorial Office 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.)

The sugar industry plays a crucial role in numerous economies worldwide, and projections indicate an increase in sugar demand in the coming years. Sugar manufacturing is a complex and highly energy-demanding process, encompassing various sub-processes, including the milling of sugarcane, clarification of raw cane juice, evaporation of syrup, crystallization of syrup, and centrifugation. The crystallization process involves extracting solid sucrose crystals from a supersaturated solution, with supersaturation being a crucial variable. Most previous review articles have focused on specific topics of the crystallization process in the sugar industry. This review aims to provide a broader and updated discussion. It explores various aspects, such as the fundamentals of the process in the sugar industry, a technological description of the three-stage operation, and an analysis of the role of supersaturation. The review examines the main process variables, the most commonly used industrial sensors, and their limitations. Additionally, it discusses the main proposals and approaches found in the literature related to monitoring, modeling, and control of crystallization in the sugar industry. The article identifies and analyzes some limitations present in the literature, including the selection of instruments for industrial monitoring, confusing references to supersaturation sensors, and the need for proper error analysis in the design of process-focused estimators. In particular, there is an emphasis on including information about error bounds for supersaturation estimators. [ABSTRACT FROM AUTHOR]

Additive manufacturing processes, such as laser beam melting, are now an integral part of industrial production. However, the development of the powder bed fusion – laser beam (PBF-LB) process is still ongoing in order to fully understand the complex interaction of the different parameters or to avoid process instabilities. As a result, process control and active parameter regulation are increasingly in the focus of current developments. A necessary prerequisite for achieving the goal of a controlled process is reliable defect detection as early as possible during a build job. In this paper a new approach is discussed: An equipment-independent image acquisition system combined with deep learning image processing. image of each layer is acquired after the build platform is recoated. The surface of the entire powder bed is evaluated by a CNN and the defect classification result is returned within seconds. The CNN is trained with a dataset of images from different real build jobs. Evaluating the entire powder bed provides the ability to detect and categorize defects with large extents. The perspective is a real-time process monitoring and control. Correction strategies are to be derived that interrupt the build process at the desired point and intervene in a targeted manner.

Control valve stiction is prevalent in the process industry, leading to oscillations in process variables and degrading unit performance. Stiction is usually repaired during scheduled plant maintenance, but until then, compensation methods are required to reduce oscillations. This study presents a novel approach to address this problem by identifying control valve and process models and optimizing control parameters. The method combines a stiction model, a process model, and a controller to achieve optimal control with stiction. Additionally, the method incorporates a controller output filter and considers undershooting during the step response for control performance assessment. The proposed method is designed to handle different types of valve stiction models, providing a versatile solution. The results show that our method significantly improves the control loop performance of all valves tested.

Advancements in coffee processing technologies have led to improved efficiency in field operations, but challenges still exist in their practical implementation. Various alternatives and solutions have been proposed to enhance processing efficiency and address issues related to safety, standardization, and quality improvement in coffee production. A literature review using SciMAT and ScientoPy software highlighted advancements in fermentation tanks and the emergence of novel fermentation methodologies. However, these innovations lack sufficient scientific evidence. Researchers are now focusing on systematic approaches, such as controlled fermentations and evaluating the influence of microorganisms and process conditions on sensory attributes and coffee composition. Brazil is the leader in coffee bean fermentation research, but the number of published papers in the field has recently decreased. Despite this, efforts continue to improve process control and optimize product quality. The study emphasizes the need for further innovation in coffee fermentation technologies to increase efficiency, sustainability, and profitability while minimizing environmental impact. Implementing these advancements promises a more sustainable and quality-driven future for the coffee industry. [ABSTRACT FROM AUTHOR]

This study explored the low-carbon operation and control strategy of wastewater treatment plants based on cost reduction and efficiency improvement, with a South China plant as a case study. Factors influencing phosphorus removal, including sludge age, concentration, dissolved oxygen, reflux ratio, and chemical dosing location, were analyzed. Optimal operation parameters such as sludge concentration, air/water ratio, and dissolved oxygen level were established based on influent characteristics across seasons. Implementation of these low-carbon strategies ensures the plant's effluent quality remained within standards, exhibiting a stable and decreasing trend. Meanwhile, chemical consumption for phosphorus removal decreasesd by approximately 41%, the electricity consumption per ton of treated wastewater decreased by 26.5%, resulting in a reduction of indirect carbon emissions by 0.18 kg/t(calculated by CO2,eq), which had achieved energy conservation, emission reduction, and clean production.

The main objective was to build a mobile impedance/capacitance tomograph for flow analysis and process optimization. The product responds to the identified needs of target customers who operate in process monitoring, control, and optimization of manufacturing processes. They need information on data processing and analysis and spatial analysis of technological processes. The designed solution's target market is the food, chemical, pharmaceutical, petrochemical, and mining industries. The paper presents the device's design and an application for data collection and visualization using tomography. Tomography is a technique for imaging a cross-section of an object based on measurements taken from the outside. Tikhonov regularisation reconstructions were used to reconstruct tomographic images. Test measurements were taken to confirm the correct operation of the completed system. Keywords: tomography, production analysis, optimization, process management, process modeling, process control.

The article presents the results of the development of indicators for monitoring the process of developing standardization documents. An integral part of the management of the standardization document development process is the monitoring system. The monitoring system allows you to monitor and control key indicators of the standardization document development process. Also, the use of a monitoring system makes it possible to form a reference base for analyzing the causes of deviations in the values of indicators. At the same time, an important task is to evaluate the effectiveness of the monitoring system and the mathematical justification of the indicator for evaluating the effectiveness of the monitoring system, taking into account errors in the implementation of the monitoring process.

The abradable coating is one of the significant stator components in aero-engine，which protects the rotating blade and casing by abrading itself and improves the engine performance by reducing the gap between the rotor and stator. In this study，taking the addition-type silicone resin as the matrix，the effect of diameter and stacking density of glass hollow/solid microspheres on the processability of abradable coating was investigated. Moreover，the effect of glass microsphere type and content was illustrated using a self-designed wear tester simulating the working conditions of stator in aero-engine. The results show that the silicone resin reinforced by H-type （size distribution 20–820 um） hollow glass microspheres exhibits excellent surface quality，flowability and comprehensive performance. The suitable preparation technology of abradable coatings was developed by glass microsphere selection，content controlling，tool design and infusion parameters optimizing. The as-prepared glass microsphere/silicone abradable composite is successfully applied and verified in aero-engine stator component.

In rubber extrusion, precise temperature control is critical due to the process's sensitivity to fluctuating parameters like compound behavior and batch-specific material variations. Rapid adjustments to temperature deviations are essential to ensure stable throughput and extrudate surface integrity. Based on our previous research, which initiated the development of a feedforward neural network (FNN) without real-world empirical application, we now present a real-time control system using artificial neural networks (ANNs) for dynamic temperature regulation. The underlying FNN was trained on a dataset comprising different ethylene propylene diene monomer (EPDM) rubber compounds, totaling 14,923 measurement points for each temperature value. After training, the FNN achieves remarkable precision, evidenced by a mean absolute percentage error (MAPE) of 0.68% and a mean squared error (MSE) of 0.63°C2 in predicting temperature variations. Its integration into the control system enables real-time responsiveness, allowing for adjustments to temperature deviations within an average timeframe of 68 ms. A key advantage over proportional-integral-derivative (PID) controllers is the ability to continuously learn and adjust to complex, non-linear, and batch-specific process dynamics. This adaptability results in enhanced process stability, as evidenced by inline manufacturing validation. Our paper presents the first ANN-based rubber extrusion control, demonstrating how machine learning techniques can be effectively leveraged for real-time, adaptive temperature control. Beyond rubber extrusion, this strategy has potential applications in various polymer processing and other industries requiring precise temperature control. Future trends may involve the integration of online learning techniques and the expansion of interconnected manufacturing processes. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy backstepping controller design to improve the control performance of GUNT RT 512 industrial liquid level system. This system performs the process of reaching the liquid in the level tank to the level reference entered by the controller on it. The original control algorithm run on the ontroller is PID. In this study, the system is tested with a classical backstepping controller and fuzzy backstepping controller. The fuzzy logic controller determines one of the most important parameters of the backstepping controller. Constant and variable liquid levels are applied to the liquid level controller system. The system's dynamic responses are evaluated on response time, settling time, overshoot, and especially steady-state error. The parameter most in need of improvement is the steady-state error for the classical backstepping controller as this controller typically offers asymptotic tracking. Furthermore, the fuzzy backstepping proposes the exact tracking. The simulation results show that improvement has been achieved in almost all performance parameters with the proposed controller. [ABSTRACT FROM AUTHOR]

Semiconductor manufacturing processing can be jeopardized due to process fluctuations, and the degradation of equipment parts can significantly influence process variation. Timely diagnosing equipment faults causing process variations is desired in current high-end product manufacturing. This paper proposes a diagnostic method for the SiH4 gas flow rate drift using N2 vibrational transition in oxide deposition. In this research, optical emission spectroscopy (OES) and quadrupole mass spectrometer (QMS) are employed as condition monitoring sensors serving as a reference model to compare the diagnostic performance for gas flow rate drift. The study observes that the OES model exhibits much higher performance for minor diagnoses of less than 5% drift. The diagnostic model performance can be enhanced by incorporating plasma condition and gas indicators compared to when these indicators are used individually. This suggests that when conducting diagnostics for equipment and processes, it is crucial to consider indirect indicators like plasma indicators along with direct indicators such as gas radical density. The comprehensive use of both types of indicators enhances the diagnostic performance, providing a more accurate assessment of the conditions and potential problem in semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

In wafer etching, regular cleaning and maintenance of process chambers are necessary to reduce particle contamination of etched wafers during the wafer transfer process. Investigating alternative cleaning and maintenance is imperative. This study analyzed the number of particles falling onto a silicon wafer when the pressure difference within the process chamber was manipulated. We observed that rapid opening of the pressure control valve, which regulates the chamber’s pressure, caused contamination during wafer transport. This was particularly true when the change in the pressure ratio was considerable. The by-products near the side of the chamber’s pressure control valve were activated and transported. We verified this finding by adjusting the opening ratio of the pressure control valve (i.e., its degree of opening). We proposed that during the transition step of the etching process, this opening ratio can be controlled by regulating the process pressure through gas flow settings. This method could suppress the deposition of reflected particles originating from the turbomolecular pump’s pumping line on wafers, thereby minimizing the contamination of wafers. [ABSTRACT FROM AUTHOR]

The biopharmaceutical industry is still running in batch mode, mostly because it is highly regulated. In the past, sensors were not readily available and in‐process control was mainly executed offline. The most important product parameters are quantity, purity, and potency, in addition to adventitious agents and bioburden. New concepts using disposable single‐use technologies and integrated bioprocessing for manufacturing will dominate the future of bioprocessing. To ensure the quality of pharmaceuticals, initiatives such as Process Analytical Technologies, Quality by Design, and Continuous Integrated Manufacturing have been established. The aim is that these initiatives, together with technology development, will pave the way for process automation and autonomous bioprocessing without any human intervention. Then, real‐time release would be realized, leading to a highly predictive and robust biomanufacturing system. The steps toward such automated and autonomous bioprocessing are reviewed in the context of monitoring and control. It is possible to integrate real‐time monitoring gradually, and it should be considered from a soft sensor perspective. This concept has already been successfully implemented in other industries and requires relatively simple model training and the use of established statistical tools, such as multivariate statistics or neural networks. This review describes a scenario for integrating soft sensors and predictive chemometrics into modern process control. This is exemplified by selective downstream processing steps, such as chromatography and membrane filtration, the most common unit operations for separation of biopharmaceuticals. [ABSTRACT FROM AUTHOR]

Copyright of Industrial Water Treatment is the property of CNOOC Tianjin Chemical Research & Design Institute 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.)

BACKGROUND: In this work, a process development study for the crystallization step of dexlansoprazole API has been demonstrated under the Quality‐by‐Design (QbD) paradigm. Dexlansoprazole (crystalline) samples prepared under different crystallization conditions of reactor temperature and addition time of anti‐solvent were subjected to solid‐state characterization with variety of analytical techniques, including X‐ray powder diffraction (XRPD), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), dynamic vapour sorption (DVS), scanning electron microscopy (SEM), particle size distribution (PSD) by laser diffraction and intrinsic dissolution rate (IDR). RESULTS: The crystalline samples were observed to have identical polymorphic phase integrity but differed in their physical characteristics. The particle size distribution and microscopy data collectively elucidated the particle growth kinetics and the extent of agglomeration could be correlated to crystallization conditions, namely reactor temperature and addition time of anti‐solvent. The intrinsic dissolution rate of samples was in good agreement with the morphological properties. CONCLUSION: An empirical model has been proposed for intrinsic dissolution rate correlating with crystallization process parameters. In conclusion, it was possible to elucidate the impact of crystallization parameters on the solid‐state behaviour of dexlansoprazole for development of appropriate pharmaceutical dosage forms. © 2024 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

When constructing long-span spatial steel structures, the unformed structure is often incomplete and unstable. The construction sequence significantly influences the mechanical state of the structure during the construction stages (CSs), affecting both the path and time effects. This study examined the mechanical properties of the construction process using an actual project as a case study, comparing two methods: one-step forming and stage-by-stage forming. Critical turning points of stress and displacement during the CSs were identified as the initial installation and unloading stages. Stress concentrations frequently occurred at temporary support points, and peak displacements often appeared at the outer overhanging bars of the structure. A well-planned construction sequence can effectively manage the structure's formation, boundaries, and loading to ensure construction safety and stability. The conclusions and analysis methods from this study provide valuable references for the design and construction of similar long-span spatial steel structures. [ABSTRACT FROM AUTHOR]

The escalating demand for renewable energy solutions and the imperative for efficient energy storage mechanisms have catalyzed the evolution of Power-to-X (PtX) technologies. Germany has the most PtX projects, accounting for 44% of all projects that have been reported. PtX encompasses a spectrum of processes engineered to convert surplus renewable energy into versatile energy carriers or chemical commodities, such as hydrogen, methane, or synthetic fuels. The Paramount to this domain is to synergistically harness renewable energy sources and hydrogen carriers to curtail waste and bolster sustainability. Central to achieving this synergy is the effective control of PtX processes, ensuring optimal performance, cost efficiency, and seamless integration within existing energy frameworks. This paper delves into the control processes and environmental prerequisites within the PtX landscape, elucidating key challenges, and strategies within this dynamic realm. While providing an initial overview of fundamental PtX process chains, this paper accentuates the critical role of geographical considerations. Subsequently, it conducts a thorough analysis of equipment modeling challenges, addressing performance metrics, operational requisites, and capital investments, particularly focusing on methanol and hydrogen production and storage. Through holistic examination, this paper endeavors to delineate the evolving landscape of PtX control processes and conditions, advancing our understanding of this expanding field. [Display omitted] • Comprehensive overview of PtX control process, challenges, strategies, and advancements. • Overview of PtX process chains and geographical conditions. • Analysis of equipment modeling challenges for methanol and hydrogen. • Addressing crucial storage challenges in PtX system. • Exploration of diverse influences on PtX control processes. [ABSTRACT FROM AUTHOR]

AbstractDesign of the process control scheme is critical for quality assurance to reduce variations in manufacturing systems. Taking semiconductor manufacturing as an example, extensive literature focuses on control optimization based on certain process models (usually linear models), which are obtained by experiments before a manufacturing process starts. However, in real applications, pre-defined models may not be accurate, especially for a complex manufacturing system. To tackle model inaccuracy, we propose a Model-Free Reinforcement Learning (MFRL) approach to conduct experiments and optimize control simultaneously according to real-time data. Specifically, we design a novel MFRL control scheme by updating the distribution of disturbances using Bayesian inference to reduce their large variations during manufacturing processes. As a result, the proposed MFRL controller is demonstrated to perform well in a nonlinear chemical mechanical planarization process when the process model is unknown. Theoretical properties are guaranteed when disturbances are additive. The numerical studies also demonstrate the efficiency of our methodology. [ABSTRACT FROM AUTHOR]

Crushing is a critical operation in mineral processing, and its efficient performance is vital for minimizing energy consumption, maximizing productivity, and maintaining product quality. However, due to variations in feed material characteristics and safety constraints, achieving the intended circuit performance can be challenging. In this study, a centralized control strategy based on a finite state machine (FSM) is developed to improve the operations of an iron ore crushing circuit. The aim is to increase productivity by manipulating the closed-side-setting (CSS) of cone crushers and the speed of an apron feeder while considering intermediate storage silo levels and cone crusher power limits, as well as product quality. A dynamic simulation was conducted to compare the proposed control strategy with the usual practice of setting CSS to a constant value. Four scenarios were analyzed based on variations in bond work index (BWI) and particle size distribution. The simulation results demonstrate that the proposed control strategy increased average productivity by 6.88% and 48.77% when compared to the operation with a constant CSS of 38 mm and 41 mm, respectively. The proposed strategy resulted in smoother oscillation without interlocking, and it maintained constant flow rates. This ultimately improved circuit reliability and predictability, leading to reduced maintenance costs. [ABSTRACT FROM AUTHOR]

This research proposes advanced model-based control strategies for a countercurrent flow plate heat exchanger in a virtual environment. A virtual environment with visual and auditory effects is designed, which requires a mathematical model describing the real dynamics of the process; this allows parallel fluid movement in different directions with hot and cold temperatures at the outlet, incorporating control monitoring interfaces as communication links between the virtual heat exchanger and control applications. A multivariable and non-linear process like the plate and countercurrent flow heat exchanger requires analysis in the controller design; therefore, this work proposes and compares two control strategies to identify the best-performing one. The first controller is based on the inverse model of the plant, with linear algebra techniques and numerical methods; the second controller is a model predictive control (MPC), which presents optimal control actions that minimize the steady-state errors and aggressive variations in the actuators, respecting the temperature constraints and the operating limits, incorporating a predictive model of the plant. The controllers are tested for different setpoint changes and disturbances, determining that they are not overshot and that the MPC controller has the shortest settling time and lowest steady-state error. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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.)

To reduce unwanted fat bloom in the manufacturing and storage of chocolates, detailed knowledge of the chemical composition and molecular mobility of the oils and fats contained is required. Although the formation of fat bloom on chocolate products has been studied for many decades with regard to its prevention and reduction, questions on the molecular level still remain to be answered. Chocolate products with nut-based fillings are especially prone to undesirable fat bloom. The chemical composition of fat bloom is thought to be dominated by the triacylglycerides of the chocolate matrix, which migrate to the chocolate’s surface and recrystallize there. Migration of oils from the fillings into the chocolate as driving force for fat bloom formation is an additional factor in the discussion. In this work, the migration was studied and confirmed by MRI, while the chemical composition of the fat bloom was measured by NMR spectroscopy and HPLC–MS, revealing the most important triacylglycerides in the fat bloom. The combination of HPLC–MS with NMR spectroscopy at 800 MHz allows for detailed chemical structure determination. A rapid routine was developed combining the two modalities, which was then applied to investigate the aging, the impact of chocolate composition, and the influence of hazelnut fillings processing parameters, such as the degree of roasting and grinding of the nuts or the mixing time, on fat bloom formation. [ABSTRACT FROM AUTHOR]

The LiteBIRD experiment is an international spaceborne mission, led by JAXA, the Japan Aerospace Exploration Agency, to observe cosmic microwave background (CMB) radiation. The satellite will be deployed to measure and characterize the signature of the primordial gravitational waves from cosmic inflation in the B-mode polarization of the CMB radiation. LiteBIRD will also explore the nature of quantum gravity as well as attempt to detect the recombination peak and reionization peak with greater than 5σ significance for a tensor-to-scalar value of r = 0.01. These observations and measurements will take place over 15 separate frequency bands in the range of 34–448 GHz. The 15 frequency channels are separated into three telescopes—low, medium, and high frequency. This paper will describe the process flow developed to fabricate the low frequency detectors, covering bands from 34 to 161 GHz. The detector wafer itself has a device side and a sky side. The device side contains the trichroic polarization sensitive sinuous antennae coupled to transition-edge sensor detectors. The skyside contains the cosmic ray mitigation structure. [ABSTRACT FROM AUTHOR]

Understanding the importance of quality assurance and quality control practices in the process control laboratory is key for operators to produce reliable data and make informed treatment decisions. [ABSTRACT FROM AUTHOR]

The assessment and diagnosis of controller performance for model‐based closed‐loop control systems has received considerable attention in recent years. A recognized factor dilapidating the controller performance is mismatching the true dynamical model of the plant and the mathematic model employed in the controller. In order to reduce the heavy effort to re‐identify the entire model, a large amount of recent works have been focusing on locating the mismatch. To further improve the mathematic model as well as controller performance, in this article, we provide a novel prediction‐based plant‐model mismatch quantification approach, which belongs to the class of moment match methodology. In particular, two cases of output prediction are considered: one‐step ahead prediction and multistep ahead prediction. Compared with existing efforts along the same line of mismatch quantification, when using the one‐step ahead prediction, our method shows an advantage of light computational complexity. On the other hand, when utilizing multistep predictions, it shows better convergence and robustness than the former, especially in the case of structural mismatches, and the long‐term prediction capability of the model is employed. With both predictions and consideration of the existence of unknown noise models in practice, we show that the plant‐model mismatch and unknown parameters of noise models can be quantified as the solution of a minimization issue that penalizes the discrepancy between the sample of plant outputs and the predictions calculated by considering the plant‐model mismatch and noise model parameters. Several examples are provided to demonstrate the effectiveness of the proposed methods, respectively. [ABSTRACT FROM AUTHOR]

Methane catalytic pyrolysis presents a promising avenue for producing CO 2 -free hydrogen. However, optimizing catalyst composition and process parameters through experimental studies has been resource-intensive. Machine learning techniques have been increasingly utilized to deepen the understanding of process mechanisms. In this study, machine learning models were developed to deepen the understanding of the effects of process control and catalyst design on CH 4 conversion performance. The optimal CH 4 conversion model achieved a testing R2 of 0.9999. To further assess the model's real-world applicability, an initial verification experiment was conducted using a catalyst composition and process parameters distinct from those in the dataset. The results reaffirmed the model's good accuracy in real-world scenarios. Delving deeper into the intricacies of methane pyrolysis, the Shapley values and partial dependence plots generated by the model were scrutinized and discussed to provide a new perspective on the influence of process control and catalyst design parameters on methane catalytic decomposition. [Display omitted] • Machine learning models are constructed for methane pyrolysis • The random forest CH 4 conversion model has a fitting accuracy of R2 = 0.9999 • A sample experiment was conducted to verify model's reliability • Importance and partial dependence analyses were performed to the optimized model • Effect of process control and catalyst design parameters were demonstrated visually [ABSTRACT FROM AUTHOR]