1
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Honti B, Farkas A, Nagy ZK, Pataki H, Nagy B. Explainable deep recurrent neural networks for the batch analysis of a pharmaceutical tableting process in the spirit of Pharma 4.0. Int J Pharm 2024; 662:124509. [PMID: 39048040 DOI: 10.1016/j.ijpharm.2024.124509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 07/19/2024] [Accepted: 07/21/2024] [Indexed: 07/27/2024]
Abstract
Due to the continuously increasing Cost of Goods Sold, the pharmaceutical industry has faced several challenges, and the Right First-Time principle with data-driven decision-making has become more pressing to sustain competitiveness. Thus, in this work, three different types of artificial neural network (ANN) models were developed, compared, and interpreted by analyzing an open-access dataset from a real pharmaceutical tableting production process. First, the multilayer perceptron (MLP) model was used to describe the total waste based on 20 raw material properties and 25 statistical descriptors of the time series data collected throughout the tableting (e.g., tableting speed and compression force). Then using 10 process time series data in addition to the raw material properties, the cumulative waste, during manufacturing was also predicted by long short-term memory (LSTM) and bidirectional LSTM (biLSTM) recurrent neural networks (RNN). The LSTM network was used to forecast the waste production profile to allow preventive actions. The results showed that RNNs were able to predict the waste trajectory, the best model resulting in 1096 and 2174 tablets training and testing root mean squared errors, respectively. For a better understanding of the process, and the models and to help the decision-support systems and control strategies, interpretation methods were implemented for all ANNs, which increased the process understanding by identifying the most influential material attributes and process parameters. The presented methodology is applicable to various critical quality attributes in several fields of pharmaceutics and therefore is a useful tool for realizing the Pharma 4.0 concept.
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Affiliation(s)
- Barbara Honti
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, 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., H-1111 Budapest, Hungary
| | - Hajnalka Pataki
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Brigitta Nagy
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary.
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2
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Alharby TN, Alanazi M. Development of advanced computational simulation of two-dimensional plate-like crystals: A comparison with population balance model. ARAB J CHEM 2023. [DOI: 10.1016/j.arabjc.2023.104832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
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3
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Computing Topological Invariants of Deep Neural Networks. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9051908. [PMID: 36248937 PMCID: PMC9568295 DOI: 10.1155/2022/9051908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 07/27/2022] [Accepted: 09/12/2022] [Indexed: 11/26/2022]
Abstract
A deep neural network has multiple layers to learn more complex patterns and is built to simulate the activity of the human brain. Currently, it provides the best solutions to many problems in image recognition, speech recognition, and natural language processing. The present study deals with the topological properties of deep neural networks. The topological index is a numeric quantity associated to the connectivity of the network and is correlated to the efficiency and accuracy of the output of the network. Different degree-related topological indices such as Zagreb index, Randic index, atom-bond connectivity index, geometric-arithmetic index, forgotten index, multiple Zagreb indices, and hyper-Zagreb index of deep neural network with a finite number of hidden layers are computed in this study.
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4
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Mora-Mariano D, Flores-Tlacuahuac A. A machine learning approach for the surrogate modeling of uncertain distributed process engineering models. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2022.07.050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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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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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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7
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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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8
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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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9
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Moon J, Gbadago DQ, Hwang G, Lee D, Hwang S. Software platform for high-fidelity-data-based artificial neural network modeling and process optimization in chemical engineering. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2021.107637] [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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10
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Ooi YJ, Aung KNG, Chong JW, Tan RR, Aviso KB, Chemmangattuvalappil NG. Design of fragrance molecules using computer-aided molecular design with machine learning. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2021.107585] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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11
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Nirschl H, Winkler M, Sinn T, Menesklou P. Autonomous Processes in Particle Technology. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Hermann Nirschl
- Karlsruher Institut für Technologie (KIT) Institut für Mechanische Verfahrenstechnik und Mechanik Strasse am Forum 8 76131 Karlsruhe Germany
| | - Marvin Winkler
- Karlsruher Institut für Technologie (KIT) Institut für Mechanische Verfahrenstechnik und Mechanik Strasse am Forum 8 76131 Karlsruhe Germany
| | - Tabea Sinn
- Karlsruher Institut für Technologie (KIT) Institut für Mechanische Verfahrenstechnik und Mechanik Strasse am Forum 8 76131 Karlsruhe Germany
| | - Philipp Menesklou
- Karlsruher Institut für Technologie (KIT) Institut für Mechanische Verfahrenstechnik und Mechanik Strasse am Forum 8 76131 Karlsruhe Germany
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12
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Optimization of Position and Number of Hotspot Detectors Using Artificial Neural Network and Genetic Algorithm to Estimate Material Levels Inside a Silo. SENSORS 2021; 21:s21134427. [PMID: 34203417 PMCID: PMC8271723 DOI: 10.3390/s21134427] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 06/23/2021] [Accepted: 06/25/2021] [Indexed: 11/16/2022]
Abstract
To realize efficient operation of a silo, level management of internal storage is crucial. In this study, to address the existing measurement limitations, a silo hotspot detector, which is typically utilized for internal silo temperature monitoring, was employed. The internal temperature data measured using the hotspot detectors were used to train an artificial neural network (ANN) algorithm to predict the level of the internal storage of the silo. The prediction accuracy was evaluated by comparing the predicted data with ground truth data. We combined the ANN model with the genetic algorithm (GA) to improve the prediction accuracy and establish efficient sensor installation positions and number to proceed with optimization. Simulation results demonstrated that the best predictive performance (up to 97% accuracy) was achieved when the ANN structure was 9-19-19-1. Furthermore, the numbers of efficient sensors and sensors positions determined using the proposed ANN-GA technique were reduced from seven to five or four, thereby ensuring economic feasibility.
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13
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Heng T, Yang D, Wang R, Zhang L, Lu Y, Du G. Progress in Research on Artificial Intelligence Applied to Polymorphism and Cocrystal Prediction. ACS OMEGA 2021; 6:15543-15550. [PMID: 34179597 PMCID: PMC8223226 DOI: 10.1021/acsomega.1c01330] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 05/28/2021] [Indexed: 06/13/2023]
Abstract
Artificial intelligence (AI) is a technology that builds an artificial system with certain intelligence and uses computer software and hardware to simulate intelligent human behavior. When combined with drug research and development, AI can considerably shorten this cycle, improve research efficiency, and minimize costs. The use of machine learning to discover novel materials and predict material properties has become a new research direction. On the basis of the current status of worldwide research on the combination of AI and crystal form and cocrystal, this mini-review analyzes and explores the application of AI in polymorphism prediction, crystal structure analysis, crystal property prediction, cocrystal former (CCF) screening, cocrystal composition prediction, and cocrystal formation prediction. This study provides insights into the future applications of AI in related fields.
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Affiliation(s)
- Tianyu Heng
- Beijing
City Key Laboratory of Polymorphic Drugs, Center of Pharmaceutical
Polymorphs, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, P.R. China
| | - Dezhi Yang
- Beijing
City Key Laboratory of Polymorphic Drugs, Center of Pharmaceutical
Polymorphs, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, P.R. China
| | - Ruonan Wang
- Beijing
City Key Laboratory of Polymorphic Drugs, Center of Pharmaceutical
Polymorphs, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, P.R. China
| | - Li Zhang
- Beijing
City Key Laboratory of Polymorphic Drugs, Center of Pharmaceutical
Polymorphs, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, P.R. China
| | - Yang Lu
- Beijing
City Key Laboratory of Polymorphic Drugs, Center of Pharmaceutical
Polymorphs, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, P.R. China
| | - Guanhua Du
- Beijing
City Key Laboratory of Drug Target and Screening Research, National
Center for Pharmaceutical Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union
Medical College, Beijing 100050, P.R. China
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14
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Lou H, Lian B, Hageman MJ. Applications of Machine Learning in Solid Oral Dosage Form Development. J Pharm Sci 2021; 110:3150-3165. [PMID: 33951418 DOI: 10.1016/j.xphs.2021.04.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/18/2021] [Accepted: 04/19/2021] [Indexed: 02/07/2023]
Abstract
This review comprehensively summarizes the application of machine learning in solid oral dosage form development over the past three decades. In both academia and industry, machine learning is increasingly applied for multiple preformulation/formulation and process development studies. Further, this review provides the authors' perspectives on how pharmaceutical scientists can use machine learning for right projects and in right ways; some key ingredients include (1) the determination of inputs, outputs, and objectives; (2) the generation of a database containing high-quality data; (3) the development of machine learning models based on dataset training and model optimization; (4) the application of trained models in making predictions for new samples. It is expected by the authors and others that machine learning will promisingly play a more important role in tomorrow's projects for solid oral dosage form development.
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Affiliation(s)
- Hao Lou
- Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS 66047, United States; Biopharmaceutical Innovation and Optimization Center, University of Kansas, Lawrence, KS 66047, United States.
| | - Bo Lian
- College of Pharmacy, University of Arizona, Tucson, AZ 85721, United States
| | - Michael J Hageman
- Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS 66047, United States; Biopharmaceutical Innovation and Optimization Center, University of Kansas, Lawrence, KS 66047, United States
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15
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Yu W, Zhu C, Tsunooka Y, Huang W, Dang Y, Kutsukake K, Harada S, Tagawa M, Ujihara T. Geometrical design of a crystal growth system guided by a machine learning algorithm. CrystEngComm 2021. [DOI: 10.1039/d1ce00106j] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
This study proposes a new high-speed method for designing crystal growth systems. It is capable of optimizing large numbers of parameters simultaneously which is difficult for traditional experimental and computational techniques.
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Affiliation(s)
- Wancheng Yu
- Institute of Materials and Systems for Sustainability (IMaSS)
- Nagoya University
- Nagoya 464-8603
- Japan
| | - Can Zhu
- Institute of Materials and Systems for Sustainability (IMaSS)
- Nagoya University
- Nagoya 464-8603
- Japan
| | - Yosuke Tsunooka
- Graduate School of Engineering
- Nagoya University
- Nagoya 464-8603
- Japan
| | - Wei Huang
- Graduate School of Engineering
- Nagoya University
- Nagoya 464-8603
- Japan
| | - Yifan Dang
- Graduate School of Engineering
- Nagoya University
- Nagoya 464-8603
- Japan
| | - Kentaro Kutsukake
- Institute of Materials and Systems for Sustainability (IMaSS)
- Nagoya University
- Nagoya 464-8603
- Japan
- Center for Advanced Intelligence Project
| | - Shunta Harada
- Institute of Materials and Systems for Sustainability (IMaSS)
- Nagoya University
- Nagoya 464-8603
- Japan
- Graduate School of Engineering
| | - Miho Tagawa
- Institute of Materials and Systems for Sustainability (IMaSS)
- Nagoya University
- Nagoya 464-8603
- Japan
- Graduate School of Engineering
| | - Toru Ujihara
- Institute of Materials and Systems for Sustainability (IMaSS)
- Nagoya University
- Nagoya 464-8603
- Japan
- Graduate School of Engineering
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16
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Hwangbo S, Al R, Sin G. An integrated framework for plant data-driven process modeling using deep-learning with Monte-Carlo simulations. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107071] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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17
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Öner M, Montes FC, Ståhlberg T, Stocks SM, Bajtner JE, Sin G. Comprehensive evaluation of a data driven control strategy: Experimental application to a pharmaceutical crystallization process. Chem Eng Res Des 2020. [DOI: 10.1016/j.cherd.2020.08.032] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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18
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Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials. CRYSTALS 2020. [DOI: 10.3390/cryst10080663] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this review, we summarize the results concerning the application of artificial neural networks (ANNs) in the crystal growth of electronic and opto-electronic materials. The main reason for using ANNs is to detect the patterns and relationships in non-linear static and dynamic data sets which are common in crystal growth processes, all in a real time. The fast forecasting is particularly important for the process control, since common numerical simulations are slow and in situ measurements of key process parameters are not feasible. This important machine learning approach thus makes it possible to determine optimized parameters for high-quality up-scaled crystals in real time.
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19
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Khezri V, Yasari E, Panahi M, Khosravi A. Hybrid Artificial Neural Network–Genetic Algorithm-Based Technique to Optimize a Steady-State Gas-to-Liquids Plant. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.9b06477] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Vahid Khezri
- Chemical Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Elham Yasari
- Chemical Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mehdi Panahi
- Chemical Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia
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20
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Dosta M, Litster JD, Heinrich S. Flowsheet simulation of solids processes: Current status and future trends. ADV POWDER TECHNOL 2020. [DOI: 10.1016/j.apt.2019.12.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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21
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Zhu Y, Yang H, Si Z, Zhang X. Solubility and thermodynamics of l-hydroxyproline in water and (methanol, ethanol, n-propanol) binary solvent mixtures. J Mol Liq 2020. [DOI: 10.1016/j.molliq.2019.112043] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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22
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Hjorth T, Svärd M, Rasmuson ÅC. Rationalising crystal nucleation of organic molecules in solution using artificial neural networks. CrystEngComm 2019. [DOI: 10.1039/c8ce01576g] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Artificial neural network modelling is used to analyse and predict primary nucleation based on various physicochemical solute and solvent parameters.
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Affiliation(s)
- Timothy Hjorth
- Department of Chemical Engineering
- KTH Royal Institute of Technology
- Stockholm
- Sweden
| | - Michael Svärd
- Department of Chemical Engineering
- KTH Royal Institute of Technology
- Stockholm
- Sweden
- Synthesis and Solid State Pharmaceutical Centre
| | - Åke C. Rasmuson
- Department of Chemical Engineering
- KTH Royal Institute of Technology
- Stockholm
- Sweden
- Synthesis and Solid State Pharmaceutical Centre
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23
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Zhang L, Mao H, Liu L, Du J, Gani R. A machine learning based computer-aided molecular design/screening methodology for fragrance molecules. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.04.018] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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24
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Picos-Benítez AR, López-Hincapié JD, Chávez-Ramírez AU, Rodríguez-García A. Artificial intelligence based model for optimization of COD removal efficiency of an up-flow anaerobic sludge blanket reactor in the saline wastewater treatment. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2017; 75:1351-1361. [PMID: 28333051 DOI: 10.2166/wst.2017.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The complex non-linear behavior presented in the biological treatment of wastewater requires an accurate model to predict the system performance. This study evaluates the effectiveness of an artificial intelligence (AI) model, based on the combination of artificial neural networks (ANNs) and genetic algorithms (GAs), to find the optimum performance of an up-flow anaerobic sludge blanket reactor (UASB) for saline wastewater treatment. Chemical oxygen demand (COD) removal was predicted using conductivity, organic loading rate (OLR) and temperature as input variables. The ANN model was built from experimental data and performance was assessed through the maximum mean absolute percentage error (= 9.226%) computed from the measured and model predicted values of the COD. Accordingly, the ANN model was used as a fitness function in a GA to find the best operational condition. In the worst case scenario (low energy requirements, high OLR usage and high salinity) this model guaranteed COD removal efficiency values above 70%. This result is consistent and was validated experimentally, confirming that this ANN-GA model can be used as a tool to achieve the best performance of a UASB reactor with the minimum requirement of energy for saline wastewater treatment.
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Affiliation(s)
- Alain R Picos-Benítez
- Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Environmental Electrochemistry, CIDETEQ, S. C. Parque Tecnológico s/n Sanfandila, Pedro Escobedo, Qro. 76703, México E-mail:
| | - Juan D López-Hincapié
- Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Environmental Electrochemistry, CIDETEQ, S. C. Parque Tecnológico s/n Sanfandila, Pedro Escobedo, Qro. 76703, México E-mail:
| | - Abraham U Chávez-Ramírez
- Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Environmental Electrochemistry, CIDETEQ, S. C. Parque Tecnológico s/n Sanfandila, Pedro Escobedo, Qro. 76703, México E-mail:
| | - Adrián Rodríguez-García
- Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Environmental Electrochemistry, CIDETEQ, S. C. Parque Tecnológico s/n Sanfandila, Pedro Escobedo, Qro. 76703, México E-mail:
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