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Wang Z, Tang S, Yang Y, Chen Y, Yang L. Data-Knowledge-Driven Modeling and Operational Adjustment for the Pharmaceutical Tablet Manufacturing Process via Wet Granulation. ACS OMEGA 2023; 8:24441-24453. [PMID: 37457484 PMCID: PMC10339337 DOI: 10.1021/acsomega.3c02199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 06/20/2023] [Indexed: 07/18/2023]
Abstract
In the context of Pharma 4.0, pharmaceutical quality control (PQC) is beset by issues such as uncertainties from ever-changing critical material attributes and strong coupling between variables in the multi-unit pharmaceutical tablet manufacturing process (PTMP), and how to timely adjust the operational variables to deal with such challenges has become a key problem in PQC. In this study, we propose a novel data-knowledge-driven modeling and operational adjustment framework for PTMP by integrating Bayesian network (BN) and case-based reasoning (CBR). At the modeling level, first, a distributed concept is introduced, i.e., the BN model for each subunit of PTMP is established in accordance with the operation process sequence, and the transition variables are given by the BN model established first and retrieved as the new query for the next unit. Once the BN models of all subunits are built, they are integrated into a global BN model. At the operational adjustment level, by taking the expected critical quality attributes (CQAs) and related prior information as evidence, the operational adjustment is achieved through global BN reasoning. Finally, the case study in a sprayed fluidized-bed granulation-based PTMP demonstrates the feasibility and effectiveness in improving the terminal CQAs of the proposed method, which is also compared with other methods to showcase its efficacy and merits.
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Affiliation(s)
- Zhengsong Wang
- School
of Control Engineering, Northeastern University
at Qinhuangdao, Qinhuangdao 066004, China
- College
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Shengnan Tang
- School
of Control Engineering, Northeastern University
at Qinhuangdao, Qinhuangdao 066004, China
- College
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Yanqiu Yang
- College
of Life and Health Sciences, Northeastern
University, Shenyang 110169, China
| | - Yeqiu Chen
- School
of Control Engineering, Northeastern University
at Qinhuangdao, Qinhuangdao 066004, China
| | - Le Yang
- School
of Control Engineering, Northeastern University
at Qinhuangdao, Qinhuangdao 066004, China
- College
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
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2
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Hao X, Huang G, Li Z, Zheng L, Zhao Y. A spatio-temporal data decoupling convolution network model for specific surface area prediction in cement grind process. ISA TRANSACTIONS 2023; 135:380-397. [PMID: 36372603 DOI: 10.1016/j.isatra.2022.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 09/18/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
The specific surface area is one of the important indicators for measuring the quality of cement products. Realizing accurate prediction for specific surface area is very important for the production scheduling of the cement industry, energy conservation and consumption reduction and improvement of cement performance. However, due to the non-linearity, uncertainty, multiple interference, dynamic time-varying delay and multi scales in cement grinding process, it is difficult to establish an accurate soft-sensing model for cement quality prediction. Aiming at the above problems, we proposed a spatio-temporal decoupling convolution neural network model (STG-DCNN) to predict specific surface area by extracting and fusing data features in temporal and spatial dimension. To complete the prediction of specific surface area, we established the temporal series map and spatial series map by the production variables data according to the mechanism of cement grinding process. Then, sliding window technique was utilized to match the time scale in temporal series map and construct variable coupling relationship in spatial series map. The prediction accuracy, robustness and superiority of the proposed method were demonstrated by experiments results implemented on the actual cement grinding quality management database in a cement production enterprise.
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Affiliation(s)
| | | | - Ze Li
- Yanshan University, Qinhuangdao, China
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3
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McDonald MN, Zhu Q, Paxton WF, Peterson CK, Tree DR. Active control of equilibrium, near-equilibrium, and far-from-equilibrium colloidal systems. SOFT MATTER 2023; 19:1675-1694. [PMID: 36790855 DOI: 10.1039/d2sm01447e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The development of top-down active control over bottom-up colloidal assembly processes has the potential to produce materials, surfaces, and objects with applications in a wide range of fields spanning from computing to materials science to biomedical engineering. In this review, we summarize recent progress in the field using a taxonomy based on how active control is used to guide assembly. We find there are three distinct scenarios: (1) navigating kinetic pathways to reach a desirable equilibrium state, (2) the creation of a desirable metastable, kinetically trapped, or kinetically arrested state, and (3) the creation of a desirable far-from-equilibrium state through continuous energy input. We review seminal works within this framework, provide a summary of important application areas, and present a brief introduction to the fundamental concepts of control theory that are necessary for the soft materials community to understand this literature. In addition, we outline current and potential future applications of actively-controlled colloidal systems, and we highlight important open questions and future directions.
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Affiliation(s)
- Mark N McDonald
- Department of Chemical Engineering, Brigham Young University, Provo, Utah, USA.
| | - Qinyu Zhu
- Department of Chemical Engineering, Brigham Young University, Provo, Utah, USA.
| | - Walter F Paxton
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah, USA
| | - Cameron K Peterson
- Department of Electrical and Computer Engineering, Brigham Young University, Provo, Utah, USA
| | - Douglas R Tree
- Department of Chemical Engineering, Brigham Young University, Provo, Utah, USA.
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4
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Algorithms for solving boundary value problems in optimal control of seeded batch crystallization processes with temperature-dependent kinetics. Chem Eng Sci 2023. [DOI: 10.1016/j.ces.2023.118517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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5
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Xiouras C, Cameli F, Quilló GL, Kavousanakis ME, Vlachos DG, Stefanidis GD. Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization. Chem Rev 2022; 122:13006-13042. [PMID: 35759465 DOI: 10.1021/acs.chemrev.2c00141] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.
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Affiliation(s)
- Christos Xiouras
- Chemical Process R&D, Crystallization Technology Unit, Janssen R&D, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Fabio Cameli
- Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, Delaware 19716, United States
| | - Gustavo Lunardon Quilló
- Chemical Process R&D, Crystallization Technology Unit, Janssen R&D, Turnhoutseweg 30, 2340 Beerse, Belgium.,Chemical and BioProcess Technology and Control, Department of Chemical Engineering, Faculty of Engineering Technology, KU Leuven, Gebroeders de Smetstraat 1, 9000 Ghent, Belgium
| | - Mihail E Kavousanakis
- School of Chemical Engineering, National Technical University of Athens, Heroon Polytechniou 9, 15780 Zografou, Greece
| | - Dionisios G Vlachos
- Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, Delaware 19716, United States
| | - Georgios D Stefanidis
- School of Chemical Engineering, National Technical University of Athens, Heroon Polytechniou 9, 15780 Zografou, Greece.,Laboratory for Chemical Technology, Ghent University; Tech Lane Ghent Science Park 125, B-9052 Ghent, Belgium
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6
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Zhong L, Gao L, Li L, Nie L, Zhang H, Sun Z, Huang R, Zhou Z, Yin W, Wang H, Zang H. Implementation of Dynamic and Static Moisture Control in Fluidized Bed Granulation. AAPS PharmSciTech 2022; 23:174. [PMID: 35739377 DOI: 10.1208/s12249-022-02334-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 06/14/2022] [Indexed: 11/30/2022] Open
Abstract
The application of process analysis and control is essential to enhance process understanding and ensure output material quality. The present study focuses on the stability of the feedback control system for a fluidized bed granulation process. Two strategies of dynamic moisture control (DMC) and static moisture control (SMC) were established based on the in-line moisture value obtained from the near-infrared sensor and control algorithm. The performance of these strategies on quality consistency control was examined using process moisture similarity analysis and principal component analysis. The stable moisture control performance and low batch-to-batch variability indicated that the DMC method was significantly better than other granulation methods. In addition, the investigation of robustness further showed that the implemented DMC method was able to produce predetermined target moisture values by varying process parameters. This study provides an advanced and simple control method for fluidized bed granulation quality assurance.
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Affiliation(s)
- Liang Zhong
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Lele Gao
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Lian Li
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Lei Nie
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Hui Zhang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Zhongyu Sun
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Ruiqi Huang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Zhaobang Zhou
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Wenping Yin
- Shandong SMA Pharmatech Co., Ltd, 165, Huabei Rd., High & New Technology Zone Zibo, Shandong, 0533, China
| | - Hui Wang
- Shandong SMA Pharmatech Co., Ltd, 165, Huabei Rd., High & New Technology Zone Zibo, Shandong, 0533, China
| | - Hengchang Zang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China. .,National Glycoengineering Research Center, Shandong University, Jinan, 250012, Shandong, China. .,Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, 250012, China.
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7
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Nagy B, Galata DL, Farkas A, Nagy ZK. Application of Artificial Neural Networks in the Process Analytical Technology of Pharmaceutical Manufacturing-a Review. AAPS J 2022; 24:74. [PMID: 35697951 DOI: 10.1208/s12248-022-00706-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/06/2022] [Indexed: 01/22/2023] Open
Abstract
Industry 4.0 has started to transform the manufacturing industries by embracing digitalization, automation, and big data, aiming for interconnected systems, autonomous decisions, and smart factories. Machine learning techniques, such as artificial neural networks (ANN), have emerged as potent tools to address the related computational tasks. These advancements have also reached the pharmaceutical industry, where the Process Analytical Technology (PAT) initiative has already paved the way for the real-time analysis of the processes and the science- and risk-based flexible production. This paper aims to assess the potential of ANNs within the PAT concept to aid the modernization of pharmaceutical manufacturing. The current state of ANNs is systematically reviewed for the most common manufacturing steps of solid pharmaceutical products, and possible research gaps and future directions are identified. In this way, this review could aid the further development of machine learning techniques for pharmaceutical production and eventually contribute to the implementation of intelligent manufacturing lines with automated quality assurance.
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Affiliation(s)
- Brigitta Nagy
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, H-1111, Hungary
| | - Dorián László Galata
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, H-1111, Hungary
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, H-1111, Hungary
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, H-1111, Hungary.
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8
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Evolutionary neural architecture search for surrogate models to enable optimization of industrial continuous crystallization process. POWDER TECHNOL 2022. [DOI: 10.1016/j.powtec.2022.117527] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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9
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Zheng Y, Wang X, Wu Z. Machine Learning Modeling and Predictive Control of the Batch Crystallization Process. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c00026] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Yingzhe Zheng
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
| | - Xiaonan Wang
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Zhe Wu
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
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10
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Aghajanian S, Ruuskanen V, Nieminen H, Laari A, Honkanen M, Koiranen T. Real-time monitoring and insights into process control of micron-sized calcium carbonate crystallization by an in-line digital microscope camera. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2021.12.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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11
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An Ultrasound Tomography Method for Monitoring CO 2 Capture Process Involving Stirring and CaCO 3 Precipitation. SENSORS 2021; 21:s21216995. [PMID: 34770301 PMCID: PMC8587525 DOI: 10.3390/s21216995] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/16/2021] [Accepted: 10/19/2021] [Indexed: 11/17/2022]
Abstract
In this work, an ultrasound computed tomography (USCT) system was employed to investigate the fast-kinetic reactive crystallization process of calcium carbonate. USCT measurements and reconstruction provided key insights into the bulk particle distribution inside the stirred tank reactor and could be used to estimate the settling rate and settling time of the particles. To establish the utility of the USCT system for dynamical crystallization processes, first, the experimental imaging tasks were carried out with the stirred solid beads, as well as the feeding and stirring of the CaCO3 crystals. The feeding region, the mixing process, and the particles settling time could be detected from USCT data. Reactive crystallization experiments for CO2 capture were then conducted. Moreover, there was further potential for quantitative characterization of the suspension density in this process. USCT-based reconstructions were investigated for several experimental scenarios and operating conditions. This study demonstrates a real-time monitoring and fault detection application of USCT for reactive crystallization processes. As a robust noninvasive and nonintrusive tool, real-time signal analysis and reconstruction can be beneficial in the development of monitoring and control systems with real-world applications for crystallization processes. A diverse range of experimental studies shown here demonstrate the versatility of the USCT system in process application, hoping to unlock the commercial and industrial utility of the USCT devices.
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12
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An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system. Front Chem Sci Eng 2021. [DOI: 10.1007/s11705-021-2058-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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13
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Model-Based Evaluation of a Data-Driven Control Strategy: Application to Ibuprofen Crystallization. Processes (Basel) 2021. [DOI: 10.3390/pr9040653] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This work presents a methodology that relies on the application of the radial basis functions network (RBF)-based feedback control algorithms to a pharmaceutical crystallization process. Within the scope of the model-based evaluation of the proposed strategy, firstly strategies for the data treatment, data structure and the training methods reflecting the possible scenarios in the industry (Moving Window, Growing Window and Golden Batch strategies) were introduced. This was followed by the incorporation of such RBF strategies within a soft sensor application and a nonlinear predictive data-driven control application. The performance of the RBF control strategies was tested for the undisturbed cases as well as in the presence of disturbances in the process. The promising results from both RBF soft sensor control and the RBF predictive control demonstrated great potential of these techniques for the control of the crystallization process. In particular, both Moving Window and Golden Batch strategies performed the best results for an RBF soft sensor, and the Growing Window outperformed the remaining methodologies for predictive control.
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14
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Application of PAT-Based Feedback Control Approaches in Pharmaceutical Crystallization. CRYSTALS 2021. [DOI: 10.3390/cryst11030221] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Crystallization is one of the important unit operations for the separation and purification of solid products in the chemical, pharmaceutical, and pesticide industries, especially for realizing high-end, high-value solid products. The precise control of the solution crystallization process determines the polymorph, crystal shape, size, and size distribution of the crystal product, which is of great significance to improve product quality and production efficiency. In order to develop the crystallization process in a scientific method that is based on process parameters and data, process analysis technology (PAT) has become an important enabling platform. In this paper, we review the development of PAT in the field of crystallization in recent years. Based on the current research status of drug crystallization process control, the monitoring methods and control strategies of feedback control in the crystallization process were systematically summarized. The focus is on the application of model-free feedback control strategies based on the solution and solid information collected by various online monitoring equipment in product engineering, including improving particle size distribution, achieving polymorphic control, and improving purity. In this paper, the challenges of feedback control strategy in the crystallization process are also discussed, and the development trend of the feedback control strategy has been prospected.
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15
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Hwangbo S, Al R, Chen X, Sin G. Integrated Model for Understanding N 2O Emissions from Wastewater Treatment Plants: A Deep Learning Approach. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:2143-2151. [PMID: 33432810 DOI: 10.1021/acs.est.0c05231] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
This study aims to demonstrate the application of deep learning to quantitatively describe long-term full-scale data observed from wastewater treatment plants (WWTPs) from the perspectives of process modeling, process analysis, and forecasting modeling. Approximately, 750,000 measurements including the influent flow rate, air flow rate, temperature, ammonium, nitrate, dissolved oxygen, and nitrous oxide (N2O) collected for more than a year from the Avedøre WWTP located in Denmark are utilized to develop a deep neural network (DNN) through supervised learning for process modeling, and the optimal DNN (R2 > 0.90) is selected for further evaluation. For process analysis, global sensitivity analysis based on variance decomposition is considered to identify the key parameters contributing to high N2O emission characteristics. For N2O forecasting, the proposed DNN-based model is compared with long short-term memory (LSTM), showing that the LSTM-based forecasting model performs significantly better than the DNN-based model (R2 > 0.94 and the root-mean-squared error is reduced by 64%). The results account for the feasibility of data-driven methods based on deep learning for quantitatively describing and understanding the rather complex N2O dynamics in WWTPs. Research into hybrid modeling concepts integrating mechanistic models of WWTPs (e.g., ASMs) with deep learning would be suggested as a future direction for monitoring N2O emissions from WWTPs.
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Affiliation(s)
- Soonho Hwangbo
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 229, 2800 Kgs. Lyngby, Denmark
- Department of Chemical Engineering, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, South Korea
| | - Resul Al
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 229, 2800 Kgs. Lyngby, Denmark
| | - Xueming Chen
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 229, 2800 Kgs. Lyngby, Denmark
| | - Gürkan Sin
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 229, 2800 Kgs. Lyngby, Denmark
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