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Salari M, Nikoo MR, Al-Mamun A, Rakhshandehroo GR, Mooselu MG. Optimizing Fenton-like process, homogeneous at neutral pH for ciprofloxacin degradation: Comparing RSM-CCD and ANN-GA. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 317:115469. [PMID: 35751268 DOI: 10.1016/j.jenvman.2022.115469] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 05/26/2022] [Accepted: 05/30/2022] [Indexed: 06/15/2023]
Abstract
Antibiotics are considered among the most non-biodegradable environmental contaminants due to their genetic resistance. Considering the importance of antibiotics removal, this study was aimed at multi-objective modeling and optimization of the Fenton-like process, homogeneous at initial circumneutral pH. Two main issues, including maximizing Ciprofloxacin (CIP) removal and minimizing sludge to iron ratio (SIR), were modeled by comparing central composite design (CCD) based on Response Surface Methodology (RSM) and hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA). Results of simultaneous optimization using ethylene diamine tetraacetic acid (EDTA) revealed that at pH ≅ 7, optimal conditions for initial CIP concentration, Fe2+ concentration, [H2O2]/[Fe2+] molar ratio, initial EDTA concentration, and reaction time were 14.9 mg/L, 9.2 mM, 3.2, 0.6 mM, and 25 min, respectively. Under these optimal conditions, CIP removal and SIR were predicted at 85.2% and 2.24 (gr/M). In the next step, multilayer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANN) were developed to model CIP and SIR. It was concluded that ANN, especially multilayer perceptron (MLP-ANN) has a decent performance in predicting response values. Additionally, multi-objective optimization of the process was performed using Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to maximize CIP removal efficiencies while minimizing SIR. NSGA-II optimization algorithm showed a reliable performance in the interaction between conflicting goals and yielded a better result than the GA algorithm. Finally, TOPSIS method with equal weights of the criteria was applied to choose the best alternative on the Pareto optimal solutions of the NSGA-II. Comparing the optimal values obtained by the multi-objective response surface optimization models (RSM-CCD) with the NSGA-II algorithm showed that the optimal variables in both models were close and, according to the absolute relative error criterion, possessed almost the same performance in the prediction of variables.
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Affiliation(s)
- Marjan Salari
- Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran
| | - Mohammad Reza Nikoo
- Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.
| | - Abdullah Al-Mamun
- Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman
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Pathak M, Pokhriyal P, Gandhi I, Khambhampaty S. Implementation of chemometrics, design of experiments and neural network analysis for prior process knowledge assessment (PPKA), failure modes and effect analysis (FMEA), scale-down model development (SDM) and process characterization for a chromatographic purification of Teriparatide. Biotechnol Prog 2022; 38:e3252. [PMID: 35340128 DOI: 10.1002/btpr.3252] [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: 11/30/2021] [Revised: 03/24/2022] [Accepted: 03/25/2022] [Indexed: 11/10/2022]
Abstract
Process understanding and characterization forms the foundation, ensuring consistent and robust biologics manufacturing process. Using appropriate modelling tools and machine learning approaches, the process data can be monitored in real time to avoid manufacturing risks. In this article, we have outlined an approach towards implementation of chemometrics and machine learning tools (neural network analysis) to model and predict the behaviour of a mixed-mode chromatography step for a biosimilar (Teriparatide) as a case study. The process development data and process knowledge was assimilated into a prior process knowledge assessment using chemometrics tools to derive important parameters critical to performance indicators (i.e. potential quality and process attributes) and to establish the severity ranking for the FMEA analysis. The characterization data of the chromatographic operation are presented alongwith the determination of the critical, key and non- key process parameters, set points, operating, process acceptance and characterized ranges. The scale-down model establishment was assessed using traditional approaches and novel approaches like batch evolution model and neural network analysis. The batch evolution model was further used to demonstrate batch monitoring through direct chromatographic data, thus demonstrating its application for continuos process verification. Assimilation of process knowledge through a structured data acquisition approach, built-in from process development to continuous process verification was demonstrated to result in a data analytics driven model that can be coupled with machine learning tools for real time process monitoring. We recommend application of these approaches with the FDA guidance on stage wise process development and validation to reduce manufacturing risks. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Mili Pathak
- R&D, Intas Pharmaceuticals Ltd. (Biopharma Division), Ahmedabad, Gujrat, India
| | - Prashant Pokhriyal
- R&D, Intas Pharmaceuticals Ltd. (Biopharma Division), Ahmedabad, Gujrat, India
| | - Irshad Gandhi
- R&D, Intas Pharmaceuticals Ltd. (Biopharma Division), Ahmedabad, Gujrat, India
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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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4
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Rizkin BA, Hartman RL. Supervised machine learning for prediction of zirconocene-catalyzed α-olefin polymerization. Chem Eng Sci 2019. [DOI: 10.1016/j.ces.2019.115224] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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5
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Okeleye AA, Betiku E. Kariya (Hildegardia barteri) seed oil extraction: comparative evaluation of solvents, modeling, and optimization techniques. CHEM ENG COMMUN 2019. [DOI: 10.1080/00986445.2018.1550397] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Adebisi A. Okeleye
- Biochemical Engineering Laboratory, Department of Chemical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
| | - Eriola Betiku
- Biochemical Engineering Laboratory, Department of Chemical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
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Yasemi M, Rahimi M, Heydarinasab A, Ardjmand M. Optimization of microfluidic gallotannic acid extraction using artificial neural network and genetic algorithm. CHEMICAL PRODUCT AND PROCESS MODELING 2017. [DOI: 10.1515/cppm-2016-0053] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract:
The current study presents the outcomes of modeling and optimizing extraction of gallotannic acid from Quercus leaves using a microfluidic system. In this study, the effects of various experimental parameters were investigated using the method of design expert. Number of experiments suggested is 31 by central composite design of Design Expert. The experimental results of design expert were analyzed by artificial neural network (ANN). Based on the results of ANN, independent variables experiment: temperature (T), flow rate ratio (FR) and pH have shown a negative effect on extraction yield (dependent variable), while the residence time (RT) has shown a positive effect. In trained network,
${R^2} = 0.9805$
and RMSE = 0.0166 shows good agreement between the predicted values of ANN and experimental results. Optimum extraction conditions, to reach maximum yield by genetic algorithms (GA), were FR = 0.53, RT = 26.4, pH = 2.06 and T = 21.44
${R^2} = 0.9805$
. The extraction yield under the optimum predicated conditions was 96.4 %, which was well matched with the experimental value 95.01 %
$\pm 0.63$
. Based on the obtained results, it was found that the ANN model could be employed successfully in estimating the gallotannic acid extraction efficiency using microfluidic extraction method.
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Agarwal H, Rathore AS, Hadpe SR, Alva SJ. Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing. Biotechnol Prog 2016; 32:1436-1443. [PMID: 27453285 DOI: 10.1002/btpr.2329] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Revised: 07/14/2016] [Indexed: 11/06/2022]
Abstract
This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R2 ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016.
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Affiliation(s)
- Harshit Agarwal
- Dept. of Chemical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India
| | - Anurag S Rathore
- Dept. of Chemical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India
| | | | - Solomon J Alva
- Research and development, Biocon research limited, Bangalore 560100, India
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8
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The Reaction Mechanism of Acetaldehyde Ammoximation to Its Oxime in the TS-1/H2O2 System. Catalysts 2016. [DOI: 10.3390/catal6070109] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
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9
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Sedghi M, Golian A, Kolahan F, Afsar A. Optimisation of broiler chicken responses from 0 to 7 d of age to dietary leucine, isoleucine and valine using Taguchi and mathematical methods. Br Poult Sci 2015; 56:696-707. [PMID: 26447759 DOI: 10.1080/00071668.2015.1096323] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Three experiments were conducted to evaluate the applicability of the Taguchi method (TM) and optimisation algorithms to optimise the branch chain amino acids (BCAA) requirements in 0 to 7 d broiler chicks. In the first experiment, the standardised digestible (SID) amino acids and apparent metabolisable energy (AME) values of maize, wheat and soya bean meal were evaluated. In the second experiment, three factors including leucine (Leu), isoleucine (Ile) and valine (Val), each at 4 levels, were selected, and an orthogonal array layout of L16 (4(3)) using TM was performed. After data collection, optimisation of average daily gain (ADG) and feed conversion ratio (FCR) were obtained using TM. The multiobjective genetic algorithm (MOGA) and random search algorithm (RSA) were also applied to predict the optimal combination of BCAA for broiler performance. In the third experiment, a growth study was conducted to evaluate the applicability of obtained optimum BCAA requirements data by TM, MOGA and RSA, and results were compared with those of birds fed with a diet formulated according to Ross 308 recommendations. In the second experiment, the TM resulted in 13.45 g/kg SID Leu, 8.5 g/kg SID Ile and 10.45 g/kg SID Val as optimum level for maximum ADG (21.57 g/bird/d) and minimum FCR (1.11 g feed/g gain) in 0- to 7-d-old broiler chickens. MOGA predicted the following combinations: SID Leu = 14.8, SID Ile = 9.1 and SID Val = 10.3 for maximum ADG (22.05) and minimum FCR (1.11). The optimisation using RSA predicted Leu = 16.0, Ile = 9.5 and Val = 10.2 for maximum ADG (22.67), and Leu = 15.5, Ile = 9.0 and Val = 10.4 to achieve minimum FCR (1.08). The validation experiment confirmed that TM, MOGA and RSA yielded optimum determination of dietary amino acid requirements and improved ADG and FCR as compared to Aviagen recommendations. However, based on the live animal validation trial, MOGA and RSA overpredicted the optimum requirement as compared to TM. In general, the results of these studies showed that the TM may be used to optimise nutrient requirements for poultry.
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Affiliation(s)
- M Sedghi
- a Animal Science Department, Faculty of Agriculture , Ferdowsi University of Mashhad , Mashhad , Iran
| | - A Golian
- a Animal Science Department, Faculty of Agriculture , Ferdowsi University of Mashhad , Mashhad , Iran
| | - F Kolahan
- b Department of Mechanical Engineering , Ferdowsi University of Mashhad , Mashhad , Iran
| | - A Afsar
- c Evonik Degussa Iran AG , Tehran , Iran
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Jiang H, She F, Du Y, Chen R, Xing W. One-step Continuous Phenol Synthesis Technology via Selective Hydroxylation of Benzene over Ultrafine TS-1 in a Submerged Ceramic Membrane Reactor. Chin J Chem Eng 2014. [DOI: 10.1016/j.cjche.2014.09.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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11
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Patil-Shinde V, Kulkarni T, Kulkarni R, Chavan PD, Sharma T, Sharma BK, Tambe SS, Kulkarni BD. Artificial Intelligence-based Modeling of High Ash Coal Gasification in a Pilot Plant Scale Fluidized Bed Gasifier. Ind Eng Chem Res 2014. [DOI: 10.1021/ie500593j] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Veena Patil-Shinde
- Chemical
Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhabha Road, Pashan, Pune 411008, India
| | - Tejas Kulkarni
- Chemical
Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhabha Road, Pashan, Pune 411008, India
| | - Rahul Kulkarni
- Chemical
Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhabha Road, Pashan, Pune 411008, India
| | - Prakash D. Chavan
- CSIR-Central Institute of Mining and Fuel Research (CIMFR), Dhanbad, Jharkhand 828108, India
| | | | - Bijay Kumar Sharma
- CSIR-Central Institute of Mining and Fuel Research (CIMFR), Dhanbad, Jharkhand 828108, India
| | - Sanjeev S. Tambe
- Chemical
Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhabha Road, Pashan, Pune 411008, India
| | - B. D. Kulkarni
- Chemical
Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhabha Road, Pashan, Pune 411008, India
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12
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Badrnezhad R, Mirza B. Modeling and optimization of cross-flow ultrafiltration using hybrid neural network-genetic algorithm approach. J IND ENG CHEM 2014. [DOI: 10.1016/j.jiec.2013.05.012] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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13
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Schunk SA, Böhmer N, Futter C, Kuschel A, Prasetyo E, Roussière T. High throughput technology: approaches of research in homogeneous and heterogeneous catalysis. CATALYSIS 2013. [DOI: 10.1039/9781849737203-00172] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
High throughput experimentation (HTE) approaches and the choice of the design of experiment (DoE) tools are discussed with regard to their convenience and applicability in homogeneous and heterogeneous catalysis as a concerted workflow. Much attention is given to diverse methodologies and strategies, which are fundamental for the experimental planning. For two target reactions in two case studies presented in this chapter, HTE methods were applied to create and evaluate catalyst libraries. A homogeneous catalyst case study is illustrated first, which deals with parallel synthesis and screening of organometallic catalysts in the polymerisation of ethylene. The second case study (heterogeneous catalysis) focuses on coherent synthesis and testing of dopant effects on the performance of oxidation catalysts in a reaction of transformation of n-butane to maleic anhydride. Supporting examples from the literature described here show that careful planning of libraries and test conditions is vital in high throughput experimentation in order to deliver meaningful results leading to performance improvements or disruptive new findings.
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Affiliation(s)
| | - Natalia Böhmer
- hte Aktiengesellschaft Kurpfalzring 104, 69123 Heidelberg, Germany
| | - Cornelia Futter
- hte Aktiengesellschaft Kurpfalzring 104, 69123 Heidelberg, Germany
| | - Andreas Kuschel
- hte Aktiengesellschaft Kurpfalzring 104, 69123 Heidelberg, Germany
| | - Eko Prasetyo
- hte Aktiengesellschaft Kurpfalzring 104, 69123 Heidelberg, Germany
| | - Thomas Roussière
- hte Aktiengesellschaft Kurpfalzring 104, 69123 Heidelberg, Germany
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14
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Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols. Food Chem 2013; 141:320-6. [PMID: 23768364 DOI: 10.1016/j.foodchem.2013.02.084] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2012] [Revised: 02/03/2013] [Accepted: 02/23/2013] [Indexed: 11/21/2022]
Abstract
In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols.
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Lobera M, Valero S, Serra J, Escolástico S, Argente E, Botti V. Optimization of ODHE membrane reactor based on mixed ionic electronic conductor using soft computing techniques. Chem Eng Sci 2011. [DOI: 10.1016/j.ces.2010.12.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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17
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Valero S, Argente E, Botti V, Serra J, Serna P, Moliner M, Corma A. DoE framework for catalyst development based on soft computing techniques. Comput Chem Eng 2009. [DOI: 10.1016/j.compchemeng.2008.08.012] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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18
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Desai KM, Survase SA, Saudagar PS, Lele S, Singhal RS. Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of fermentative production of scleroglucan. Biochem Eng J 2008. [DOI: 10.1016/j.bej.2008.05.009] [Citation(s) in RCA: 287] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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19
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Istadi I, Amin NAS. Modelling and optimization of catalytic–dielectric barrier discharge plasma reactor for methane and carbon dioxide conversion using hybrid artificial neural network—genetic algorithm technique. Chem Eng Sci 2007. [DOI: 10.1016/j.ces.2007.07.066] [Citation(s) in RCA: 58] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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20
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Mukherjee P, Bhaumik A, Kumar R. Eco-friendly, Selective Hydroxylation of C-7 Aromatic Compounds Catalyzed by TS-1/H2O2 System under Solvent-free Solid−Liquid−Liquid-Type Triphase Conditions. Ind Eng Chem Res 2007. [DOI: 10.1021/ie070088q] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- P. Mukherjee
- Catalysis Division, National Chemical Laboratory, Pune−411 008, India
| | - Asim Bhaumik
- Catalysis Division, National Chemical Laboratory, Pune−411 008, India
| | - Rajiv Kumar
- Catalysis Division, National Chemical Laboratory, Pune−411 008, India
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Maier WF, Stöwe K, Sieg S. Combinatorial and High-Throughput Materials Science. Angew Chem Int Ed Engl 2007; 46:6016-67. [PMID: 17640024 DOI: 10.1002/anie.200603675] [Citation(s) in RCA: 271] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
There is increasing acceptance of high-throughput technologies for the discovery, development, and optimization of materials and catalysts in industry. Over the years, the relative synchronous development of technologies for parallel synthesis and characterization has been accompanied by developments in associated software and information technologies. This Review aims to provide a comprehensive overview on the state of the art of the field by selected examples. Technologies developed to aid research on complex materials are covered as well as databases, design of experiment, data-mining technologies, modeling approaches, and evolutionary strategies for development. Different methods for parallel synthesis provide single sample libraries, gradient libraries for electronic or optical materials, similar to polymers and catalysts, and products produced through formulation strategies. Many examples illustrate the variety of isolated solutions and document the barely recognized variety of new methods for the synthesis and analysis of almost any material. The Review ends with a summary of success stories and statements on still-present problems and future tasks.
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Affiliation(s)
- Wilhelm F Maier
- Technische Chemie, Universität des Saarlandes, Gebäude C4.2, 66123 Saarbrücken, Germany.
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22
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Maier W, Stöwe K, Sieg S. Kombinatorische und Hochdurchsatz-Techniken in der Materialforschung. Angew Chem Int Ed Engl 2007. [DOI: 10.1002/ange.200603675] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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23
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Istadi, Amin NAS. Hybrid Artificial Neural Network−Genetic Algorithm Technique for Modeling and Optimization of Plasma Reactor. Ind Eng Chem Res 2006. [DOI: 10.1021/ie060562c] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Istadi
- Chemical Reaction Engineering and Catalysis (CREC) Group, Department of Chemical Engineering, Diponegoro University, Jln. Prof. Sudharto, Semarang, Indonesia 50239, and Faculty of Chemical and Natural Resources Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia
| | - N. A. S. Amin
- Chemical Reaction Engineering and Catalysis (CREC) Group, Department of Chemical Engineering, Diponegoro University, Jln. Prof. Sudharto, Semarang, Indonesia 50239, and Faculty of Chemical and Natural Resources Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia
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24
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Negro C, Alonso Á, Blanco Á, Tijero J. Optimization of the Fiber Cement Composite Process. Ind Eng Chem Res 2005. [DOI: 10.1021/ie048907j] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Carlos Negro
- Department of Chemical Engineering, Complutense University of Madrid, Avda. Complutense s/n, 28040 Madrid, Spain
| | - Álvaro Alonso
- Department of Chemical Engineering, Complutense University of Madrid, Avda. Complutense s/n, 28040 Madrid, Spain
| | - Ángeles Blanco
- Department of Chemical Engineering, Complutense University of Madrid, Avda. Complutense s/n, 28040 Madrid, Spain
| | - Julio Tijero
- Department of Chemical Engineering, Complutense University of Madrid, Avda. Complutense s/n, 28040 Madrid, Spain
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