Special Issue "Modeling, Control, and Optimization of Batch and Batch-Like Processes"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Supervision".

Deadline for manuscript submissions: closed (31 May 2021).

Special Issue Editors

Prof. Dr. Prashant Mhaskar
E-Mail Website
Guest Editor
Department of Chemical Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada
Interests: data-driven modeling and control; nonlinear control; model predictive control
Dr. Joseph Sang-Il Kwon
E-Mail Website
Guest Editor
Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77845, USA
Interests: multiscale modeling; model reduction; model predictive control; machine learning; hydraulic fracturing; pulp and paper manufacturing; crystallization; cell signal pathway; protein-ligand binding; quantum dots
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Special Issue Information

Dear Colleagues,

This Special Issue will focus on the modeling, control, optimization, and design of batch and batch-like processes. Papers that focus on theoretical aspects, complex simulation case studies, or experimental or industrial applications are welcome.

Prof. Dr. Prashant Mhaskar
Prof. Dr. Joseph Sang-Il Kwon
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • batch processes
  • data-driven modeling
  • first principles modeling
  • control
  • quality control
  • monitoring, optimization, and design of batch processes.

Published Papers (9 papers)

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Research

Article
Polymethyl Methacrylate Quality Modeling with Missing Data Using Subspace Based Model Identification
Processes 2021, 9(10), 1691; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9101691 - 22 Sep 2021
Viewed by 298
Abstract
This paper addresses the problem of quality modeling in polymethyl methacrylate (PMMA) production. The key challenge is handling the large amounts of missing quality measurements in each batch due to the time and cost sensitive nature of the measurements. To this end, a [...] Read more.
This paper addresses the problem of quality modeling in polymethyl methacrylate (PMMA) production. The key challenge is handling the large amounts of missing quality measurements in each batch due to the time and cost sensitive nature of the measurements. To this end, a missing data subspace algorithm that adapts nonlinear iterative partial least squares (NIPALS) algorithms from both partial least squares (PLS) and principal component analysis (PCA) is utilized to build a data driven dynamic model. The use of NIPALS algorithms allows for the correlation structure of the input–output data to minimize the impact of the large amounts of missing quality measurements. These techniques are utilized in a simulated case study to successfully model the PMMA process in particular, and demonstrate the efficacy of the algorithm to handle the quality prediction problem in general. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Batch and Batch-Like Processes)
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Article
Backstepping Methodology to Troubleshoot Plant-Wide Batch Processes in Data-Rich Industrial Environments
Processes 2021, 9(6), 1074; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9061074 - 20 Jun 2021
Viewed by 769
Abstract
Troubleshooting batch processes at a plant-wide level requires first finding the unit causing the fault, and then understanding why the fault occurs in that unit. Whereas in the literature case studies discussing the latter issue abound, little attention has been given so far [...] Read more.
Troubleshooting batch processes at a plant-wide level requires first finding the unit causing the fault, and then understanding why the fault occurs in that unit. Whereas in the literature case studies discussing the latter issue abound, little attention has been given so far to the former, which is complex for several reasons: the processing units are often operated in a non-sequential way, with unusual series-parallel arrangements; holding vessels may be required to compensate for lack of production capacity, and reacting phenomena can occur in these vessels; and the evidence of batch abnormality may be available only from the end unit and at the end of the production cycle. We propose a structured methodology to assist the troubleshooting of plant-wide batch processes in data-rich environments where multivariate statistical techniques can be exploited. Namely, we first analyze the last unit wherein the fault manifests itself, and we then step back across the units through the process flow diagram (according to the manufacturing recipe) until the fault cannot be detected by the available field sensors any more. That enables us to isolate the unit wherefrom the fault originates. Interrogation of multivariate statistical models for that unit coupled to engineering judgement allow identifying the most likely root cause of the fault. We apply the proposed methodology to troubleshoot a complex industrial batch process that manufactures a specialty chemical, where productivity was originally limited by unexplained variability of the final product quality. Correction of the fault allowed for a significant increase in productivity. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Batch and Batch-Like Processes)
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Article
Multiscale Modeling and Recurrent Neural Network Based Optimization of a Plasma Etch Process
Processes 2021, 9(1), 151; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9010151 - 14 Jan 2021
Cited by 1 | Viewed by 596
Abstract
In this article, we focus on the development of a multiscale modeling and recurrent neural network (RNN) based optimization framework of a plasma etch process on a three-dimensional substrate with uniform thickness using the inductive coupled plasma (ICP). Specifically, the gas flow and [...] Read more.
In this article, we focus on the development of a multiscale modeling and recurrent neural network (RNN) based optimization framework of a plasma etch process on a three-dimensional substrate with uniform thickness using the inductive coupled plasma (ICP). Specifically, the gas flow and chemical reactions of plasma are simulated by a macroscopic fluid model. In addition, the etch process on the substrate is simulated by a kinetic Monte Carlo (kMC) model. While long time horizon optimization cannot be completed due to the computational complexity of the simulation models, RNN models are applied to approximate the fluid model and kMC model. The training data of RNN models are generated by open-loop simulations of the fluid model and the kMC model. Additionally, the stochastic characteristic of the kMC model is presented by a probability function. The well-trained RNN models and the probability function are then implemented in computing an open-loop optimization problem, in which a moving optimization method is applied to overcome the error accumulation problem when using RNN models. The optimization goal is to achieve the desired average etching depth and average bottom roughness within the least amount of time. The simulation results show that our prediction model is accurate enough and the optimization objectives can be completed well. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Batch and Batch-Like Processes)
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Article
Subspace Based Model Identification for an Industrial Bioreactor: Handling Infrequent Sampling Using Missing Data Algorithms
Processes 2020, 8(12), 1686; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8121686 - 21 Dec 2020
Cited by 1 | Viewed by 652
Abstract
This manuscript addresses the problem of modeling an industrial (Sartorius) bioreactor using process data. In the context of the Sartorius Bioreactor, it is important to appropriately address the problem of dealing with a large number of variables, which are not always measured or [...] Read more.
This manuscript addresses the problem of modeling an industrial (Sartorius) bioreactor using process data. In the context of the Sartorius Bioreactor, it is important to appropriately address the problem of dealing with a large number of variables, which are not always measured or are measured at different sampling rates, without taking recourse to simpler interpolation- or imputation-based approaches. To this end, a dynamic model for the Sartorius Bioreactor is developed via appropriately adapting a recently presented subspace model identification technique, which in turn uses nonlinear iterative partial least squares (NIPALS) algorithms to gracefully handle the missing data. The other key contribution is evaluating the ability of the identification approach to provide insight into the process by computing interpretable variables such as metabolite rates. The results demonstrate the ability of the proposed approach to model data from the Sartorius Bioreactor. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Batch and Batch-Like Processes)
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Article
Simulation of the Reactivation of Partially Inactivated Biocatalysts in Sequential Batch Reactors
Processes 2020, 8(11), 1419; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8111419 - 08 Nov 2020
Viewed by 833
Abstract
The enzymatic reactivation process enables the recovery of catalytic activity for inactive biocatalysts. However, its effect on the specific productivity of the processes has not been studied. The main objective of this work was to evaluate the specific productivity of the processes with [...] Read more.
The enzymatic reactivation process enables the recovery of catalytic activity for inactive biocatalysts. However, its effect on the specific productivity of the processes has not been studied. The main objective of this work was to evaluate the specific productivity of the processes with and without reactivation using the program Spyder Python (3.7). Using fixed values for all of the parameters, the global specific productivity was 8 mM/h·gbiocat for the process without reactivation, and 4 mM/h·gbiocat for the process with reactivation. Random numbers were generated to use as different values for parameters, and the results yielded a global specific productivity of 3.79 mM/h·gbiocat for the process with reactivation and 3.68 mM/h·gbiocat for the process without reactivation. ANOVA tests showed that there were significant differences between the specific global productivities of the two processes. Reactivation has great potential for use when the biocatalyst is of high cost. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Batch and Batch-Like Processes)
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Article
Dynamic Optimization of a Fed-Batch Nosiheptide Reactor
Processes 2020, 8(5), 587; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8050587 - 15 May 2020
Viewed by 1454
Abstract
Nosiheptide is a sulfur-containing peptide antibiotic, showing exceptional activity against critical pathogens such as methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE) with livestock applications that can be synthesized via fed-batch fermentation. A simplified mechanistic fed-batch fermentation model for nosiheptide production considers temperature- [...] Read more.
Nosiheptide is a sulfur-containing peptide antibiotic, showing exceptional activity against critical pathogens such as methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE) with livestock applications that can be synthesized via fed-batch fermentation. A simplified mechanistic fed-batch fermentation model for nosiheptide production considers temperature- and pH-dependence of biomass growth, substrate consumption, nosiheptide production and oxygen mass transfer into the broth. Herein, we perform dynamic simulation over a broad range of possible feeding policies to understand and visualize the region of attainable reactor performances. We then formulate a dynamic optimization problem for maximization of nosiheptide production for different constraints of batch duration and operability limits. A direct method for dynamic optimization (simultaneous strategy) is performed in each case to compute the optimal control trajectories. Orthogonal polynomials on finite elements are used to approximate the control and state trajectories allowing the continuous problem to be converted to a nonlinear program (NLP). The resultant large-scale NLP is solved using IPOPT. Optimal operation requires feedrate to be manipulated in such a way that the inhibitory mechanism of the substrate can be avoided, with significant nosiheptide yield improvement realized. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Batch and Batch-Like Processes)
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Article
Multi-Size Proppant Pumping Schedule of Hydraulic Fracturing: Application to a MP-PIC Model of Unconventional Reservoir for Enhanced Gas Production
Processes 2020, 8(5), 570; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8050570 - 12 May 2020
Cited by 6 | Viewed by 1014
Abstract
Slickwater hydraulic fracturing is becoming a prevalent approach to economically recovering shale hydrocarbon. It is very important to understand the proppant’s transport behavior during slickwater hydraulic fracturing treatment for effective creation of a desired propped fracture geometry. The currently available models are either [...] Read more.
Slickwater hydraulic fracturing is becoming a prevalent approach to economically recovering shale hydrocarbon. It is very important to understand the proppant’s transport behavior during slickwater hydraulic fracturing treatment for effective creation of a desired propped fracture geometry. The currently available models are either oversimplified or have been performed at limited length scales to avoid high computational requirements. Another limitation is that the currently available hydraulic fracturing simulators are developed using only single-sized proppant particles. Motivated by this, in this work, a computationally efficient, three-dimensional, multiphase particle-in-cell (MP-PIC) model was employed to simulate the multi-size proppant transport in a field-scale geometry using the Eulerian–Lagrangian framework. Instead of tracking each particle, groups of particles (called parcels) are tracked, which allows one to simulate the proppant transport in field-scale geometries at an affordable computational cost. Then, we found from our sensitivity study that pumping schedules significantly affect propped fracture surface area and average fracture conductivity, thereby influencing shale gas production. Motivated by these results, we propose an optimization framework using the MP-PIC model to design the multi-size proppant pumping schedule that maximizes shale gas production from unconventional reservoirs for given fracturing resources. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Batch and Batch-Like Processes)
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Article
Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes
Processes 2020, 8(2), 164; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8020164 - 02 Feb 2020
Cited by 2 | Viewed by 854
Abstract
To implement the quality-relevant monitoring scheme for batch processes with multiple output modes, this paper presents a novel methodology based on stochastic programming. Bringing together tools from stochastic programming and ensemble learning, the developed methodology focuses on the robust monitoring of process quality-relevant [...] Read more.
To implement the quality-relevant monitoring scheme for batch processes with multiple output modes, this paper presents a novel methodology based on stochastic programming. Bringing together tools from stochastic programming and ensemble learning, the developed methodology focuses on the robust monitoring of process quality-relevant variables by taking the stochastic nature of batch process parameters explicitly into consideration. To handle the problem of missing data and lack of historical batch data, a bagging approach is introduced to generate individual quality-relevant sub-datasets, which are used to construct the corresponding monitoring sub-models. For each model, stochastic programming is used to construct an optimal quality trajectory, which is regarded as the reference for online quality monitoring. Then, for each sub-model, a corresponding control limit is obtained by computing historical residuals between the actual output and the optimal trajectory. For online monitoring, the current sample is examined by all sub-models, and whether the monitoring statistic exceeds the control limits is recorded for further analysis. The final step is ensemble learning via Bayesian fusion strategy, which is under the probabilistic framework. The implementation and effectiveness of the developed methodology are demonstrated through two case studies, including a numerical example, and a simulated fed-batch penicillin fermentation process. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Batch and Batch-Like Processes)
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Article
Integrating Feedback Control and Run-to-Run Control in Multi-Wafer Thermal Atomic Layer Deposition of Thin Films
Processes 2020, 8(1), 18; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8010018 - 21 Dec 2019
Cited by 5 | Viewed by 1377
Abstract
There is currently a lack of understanding of the deposition profile in a batch atomic layer deposition (ALD) process. Also, no on-line control scheme has been proposed to resolve the prevalent disturbances. Motivated by this, we develop a computational fluid dynamics (CFD) model [...] Read more.
There is currently a lack of understanding of the deposition profile in a batch atomic layer deposition (ALD) process. Also, no on-line control scheme has been proposed to resolve the prevalent disturbances. Motivated by this, we develop a computational fluid dynamics (CFD) model and an integrated online run-to-run and feedback control scheme. Specifically, we analyze a furnace reactor for a SiO2 thin-film ALD with BTBAS and ozone as precursors. Initially, a high-fidelity 2D axisymmetric multiscale CFD model is developed using ANSYS Fluent for the gas-phase characterization and the surface thin-film deposition, based on a kinetic Monte-Carlo (kMC) model database. To deal with the disturbance during reactor operation, a proportional integral (PI) control scheme is adopted, which manipulates the inlet precursor concentration to drive the precursor partial pressure to the set-point, ensuring the complete substrate coverage. Additionally, the CFD model is utilized to investigate a wide range of operating conditions, and a regression model is developed to describe the relationship between the half-cycle time and the feed flow rate. A run-to-run (R2R) control scheme using an exponentially weighted moving average (EWMA) strategy is developed to regulate the half-cycle time for the furnace ALD process between batches. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Batch and Batch-Like Processes)
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