Processes

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20 pages, 5324 KiB

Open AccessArticle

A Feedback Control Strategy for a Fed-Batch Monoclonal Antibody Production Process Utilising Infrequent and Irregular Sampled Measurements

by Lydia Joynes and Jie Zhang

Processes 2022, 10(8), 1448; https://doi.org/10.3390/pr10081448 - 24 Jul 2022

Cited by 1 | Viewed by 1587

Abstract

The ability to take non-invasive Raman measurements presents a unique opportunity to use one Raman probe across multiple vessels in parallel, reducing costs but making measurements infrequent. Under these conditions, infrequent and irregular feedback signals can result in poor closed-loop control performance. This [...] Read more.

The ability to take non-invasive Raman measurements presents a unique opportunity to use one Raman probe across multiple vessels in parallel, reducing costs but making measurements infrequent. Under these conditions, infrequent and irregular feedback signals can result in poor closed-loop control performance. This study addressed the issue of infrequent and irregular Raman measurements using a linear dynamic model developed from interpolated data to predict more frequent measurements of the controlled variable. The simulated monoclonal antibody production was sampled hourly with white noise added to the simulated glucose concentration to replicate real Raman measurements. The hourly samples were interpolated into 15 min intervals and a linear dynamic model was developed to predict the glucose concentration at 15 min intervals. These predicted values were then used in a feedback control loop by using model predictive control or a conventional proportional and integral controller to control the glucose concentration at 15 min sampling intervals. For setpoint tracking, the model predictive control reduced the integral of absolute errors to 14,600 from 15,900 (with a 1 h sampling time) or 8.2% reduction. With adaptive model predictive control, the integral of absolute errors was reduced from 14,500 (1 h sampling time) to 14,200 for setpoint tracking and from 13,500 (1 h sampling time) to 13,300 for disturbance rejection. A final comparison demonstrated that the proposed method can also cope with random variations in the sampling time. Full article

(This article belongs to the Special Issue Modeling, Control, and Optimization of Batch and Batch-Like Processes)

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11 pages, 845 KiB

Open AccessFeature PaperArticle

Polymethyl Methacrylate Quality Modeling with Missing Data Using Subspace Based Model Identification

by Nikesh Patel, Kavitha Sivanathan and Prashant Mhaskar

Processes 2021, 9(10), 1691; https://doi.org/10.3390/pr9101691 - 22 Sep 2021

Cited by 1 | Viewed by 1474

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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19 pages, 3995 KiB

Open AccessFeature PaperArticle

Backstepping Methodology to Troubleshoot Plant-Wide Batch Processes in Data-Rich Industrial Environments

by Federico Zuecco, Matteo Cicciotti, Pierantonio Facco, Fabrizio Bezzo and Massimiliano Barolo

Processes 2021, 9(6), 1074; https://doi.org/10.3390/pr9061074 - 20 Jun 2021

Cited by 1 | Viewed by 2524

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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17 pages, 2852 KiB

Open AccessFeature PaperArticle

Multiscale Modeling and Recurrent Neural Network Based Optimization of a Plasma Etch Process

by Tianqi Xiao and Dong Ni

Processes 2021, 9(1), 151; https://doi.org/10.3390/pr9010151 - 14 Jan 2021

Cited by 7 | Viewed by 3084

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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15 pages, 1391 KiB

Open AccessFeature PaperArticle

Subspace Based Model Identification for an Industrial Bioreactor: Handling Infrequent Sampling Using Missing Data Algorithms

by Nikesh Patel, Brandon Corbett, Johan Trygg, Chris McCready and Prashant Mhaskar

Processes 2020, 8(12), 1686; https://doi.org/10.3390/pr8121686 - 21 Dec 2020

Cited by 6 | Viewed by 2347

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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12 pages, 3236 KiB

Open AccessFeature PaperArticle

Simulation of the Reactivation of Partially Inactivated Biocatalysts in Sequential Batch Reactors

by Nadia Guajardo, Fernando A. Crespo and Rodrigo A. Schrebler

Processes 2020, 8(11), 1419; https://doi.org/10.3390/pr8111419 - 8 Nov 2020

Viewed by 2126

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·g_biocat for the process without reactivation, and 4 mM/h·g_biocat 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·g_biocat for the process with reactivation and 3.68 mM/h·g_biocat 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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23 pages, 6597 KiB

Open AccessArticle

Dynamic Optimization of a Fed-Batch Nosiheptide Reactor

by Alistair D. Rodman, Samir Diab and Dimitrios I. Gerogiorgis

Processes 2020, 8(5), 587; https://doi.org/10.3390/pr8050587 - 15 May 2020

Cited by 2 | Viewed by 3876

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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17 pages, 1076 KiB

Open AccessFeature PaperArticle

Multi-Size Proppant Pumping Schedule of Hydraulic Fracturing: Application to a MP-PIC Model of Unconventional Reservoir for Enhanced Gas Production

by Prashanth Siddhamshetty, Shaowen Mao, Kan Wu and Joseph Sang-Il Kwon

Processes 2020, 8(5), 570; https://doi.org/10.3390/pr8050570 - 12 May 2020

Cited by 29 | Viewed by 3875

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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19 pages, 3801 KiB

Open AccessArticle

Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes

by Feifan Shen, Jiaqi Zheng, Lingjian Ye and De Gu

Processes 2020, 8(2), 164; https://doi.org/10.3390/pr8020164 - 2 Feb 2020

Cited by 5 | Viewed by 2755

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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18 pages, 2197 KiB

Open AccessFeature PaperArticle

Integrating Feedback Control and Run-to-Run Control in Multi-Wafer Thermal Atomic Layer Deposition of Thin Films

by Yichi Zhang, Yangyao Ding and Panagiotis D. Christofides

Processes 2020, 8(1), 18; https://doi.org/10.3390/pr8010018 - 21 Dec 2019

Cited by 14 | Viewed by 4236

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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