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modeling and optimization of the flocculation processes for removal of cationic and anionic dyes from water by an amphoteric grafting chitosan-based flocculant using response surface methodology

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2015年第17期22卷 13038-13048页

作者： Wu, Hu Yang, Ran Li, Ruihua Long, Chao Yang, Hu Li, Aimin Nanjing Univ Sch Environm State Key Lab Pollut Control & Resource Reuse Nanjing 210023 Jiangsu Peoples R China

In this study, an amphoteric grafting chitosan-based flocculant (carboxymethyl chitosan-graft-poly(2-methacryloyloxyethyl) trimethyl ammonium chloride, denoted as CMC-g-PDMC) was applied to removal of the anionic and cationic dyes, acid Green 25 (AG25) and Basic Bright Yellow (7GL), from water. Flocculation conditions have been optimized by response surface methodology (RSM) on the basis of central composite design (CCD) using flocculant dosage, initial solution pH and temperature as input variables. The second-order and cubic regression models, which have been both tested by the analysis of variance (ANOVA), were constructed to link the output response (the dye removal factor) with the aforementioned input variables, respectively. The second-order regression model well described the process of AG25 removal, whereas the cubic one is more suitable for that of 7GL. The effects of those variables on the flocculation performance of CMC-g-PDMC for removal of the two dyes containing opposite charges from aqueous solutions have been studied, and the flocculation mechanisms including the interactive effects between various influencing factors have been discussed in detail also.

关键词： Amphoteric grafting chitosan-based flocculants Dye removal Flocculation performance Response surface methodology modeling and optimization Flocculation mechanism

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modeling and optimization of coagulant dosage in water treatment plants using hybridized random forest model with genetic algorithm optimization

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2023年第10期25卷 11189-11207页

作者： Achite, Mohammed Samadianfard, Saeed Elshaboury, Nehal Sharafi, Milad Hassiba Benbouali Univ Chlef Fac Nat & Life Sci Water & Environm Lab Chlef 02180 Algeria Univ Tabriz Fac Agr Dept Water Engn Tabriz *** Iran Housing & Bldg Natl Res Ctr Construct & Project Management Res Inst Giza 12311 Egypt Urmia Univ Fac Agr Dept Water Engn Orumiyeh Iran

In water treatment plants (WTPs), the most common processes are coagulation and flocculation. Determination of the coagulant dosage is one of the most difficult procedures in the water treatment process, and it is commonly determined using the jar test technique. Given that this approach is time-consuming, expensive, prone to human error, and greatly influenced by raw water quality changes, this research presents a prediction model to make proactive decisions about coagulant doses based on changes in raw water characteristics. The model eliminates the need to employ expensive chemicals for jar testing regularly and allows for rapid response to abrupt changes in water quality. The forecasting model is developed using standalone random forest (RF) and hybridized RF model with genetic algorithm (GA) optimization, namely GA-RF, to simulate coagulant dose in the Sidi Yacoub WTP in Algeria. Different input scenarios are used in the development of the conventional and hybrid models to determine the best input combination. This study establishes the most efficient model for assessing the coagulation process using four evaluation metrics;correlation coefficient (CC), scattered index (SI), Willmott's index of agreement (WI), and mean absolute percentage error (MAPE). According to the findings, the GA-RF model (CC = 0.975, SI = 0.150, WI = 0.986, and MAPE = 7.9), which accounts for raw water production, turbidity water, conductivity, and suspended materials input parameters, outperforms the other models. The proposed model will help operators to not only reduce costs and time spent performing experimental jar testing but also to anticipate optimum coagulant dose and project water quality under real-world conditions.

关键词： modeling and optimization Coagulant dosage Turbidity removal Water treatment Random forest Genetic algorithm

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modeling and optimization of biomass supply chains: A review and a critical look

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IFAC-PapersOnLine 2016年第12期49卷 604-615页

作者： Atashbar, N. Zandi Labadie, N. Prins, C. CS 42060 Troyes10004 France

Due to the effect of greenhouse gas emissions and increasing energy demand, consumption of more sustainable and renewable energies has increased. Biofuels derived from biomass can play a key-role nowadays as one of the main sources of renewable energies. Therefore, more and more researchers have been involved in modeling and optimizing biomass supply chains. This paper presents an overview of biomass supply chains. After an outline of the main definitions and a description of typical activities in biomass supply chain; a classification of optimization methods and models developed in this context is detailed. All selected papers are classified and discussed according to (1) objective functions, (2) decision levels and (3) solution methods. Finally, a critical look at current researches and possible new directions are presented. © 2016

关键词： Biomass Biofuels Gas emissions Greenhouse effect Greenhouse gases Logistics Models optimization Supply chains Biomass supply chain Decision levels Energy demands modeling and optimization Objective functions optimization method Renewable energies Solution methods

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modeling and optimization of a plastic thermoforming process

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JOURNAL OF REINFORCED PLASTICS AND COMPOSITES 2004年第1期23卷 109-121页

作者： Yang, CY Hung, SW Natl Chiao Tung Univ Inst Business & Management Taipei Taiwan Ming Chuan Univ Dept Int Business Taipei 3 Taiwan

Thermoforming of plastic sheets has become an important process in industry because of their low cost and good formability. However there are some unsolved problems that confound the overall success of this technique. Nonuniform thickness distribution caused by inappropriate processing condition is one of them. In this study, results of experimentation were used to develop a process model for thermoforming process via a supervised learning back propagation neural network. An "inverse" neural network model was proposed to predict the optimum processing conditions. The network inputs included the thickness distribution at different positions of molded parts. The output of the processing parameters was obtained by neural computing. Good agreement was reached between the computed result by neural network and the experimental data. Optimum processing parameters can thus be obtained by using the neural network scheme we proposed. This provides significant advantages in terms of improved product quality.

关键词： inverse back propagation neural network thermoforming modeling and optimization processing parameter

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modeling and optimization of Hybrid Systems

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IFAC-PapersOnLine 2015年第27期48卷 351-357页

作者： Lennartson, Bengt Wigström, Oskar Riazi, Sarmad Bengtsson, Kristofer Department of Signals and Systems Chalmers University of Technology GöteborgSE-412 96 Sweden

In this paper a predicate transition model for discrete event systems is generalized to include continuous dynamics, and the result is a modular hybrid predicate transition model. Based on this model a hybrid Petri net, including explicit differential equations and shared variables, is also proposed. It is then shown how this hybrid Petri net model can be optimized based on a simple and robust nonlinear programming formulation. The procedure only assumes that desired sampled paths for a number of interacting moving devices are given, while originally equidistant time instances are adjusted to minimize a given criterion. This optimization of hybrid systems is also applied to a real robot station with interacting devices, which results in about 30% reduction in energy consumption. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Discrete event simulation Differential equations Energy utilization Hybrid systems Nonlinear programming optimization Petri nets Continuous dynamics Given criterion Hybrid Petri net modeling and optimization Real robot Shared variables Time instances Transition model

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modeling and optimization of Tool Wear and Surface Roughness in Turning of Al/SiCp Using Response Surface Methodology

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3D RESEARCH 2018年第4期9卷 1-13页

作者： Laghari, Rashid Ali Li, Jianguang Xie, Zhengyou Wang, Shu-qi Harbin Inst Technol Sch Mechatron Engn Harbin 150001 Heilongjiang Peoples R China

Nowadays metal matrix composites are widely utilized in major industries such as aerospace and automotive because of their excellent properties in association with non-reinforced. This research work is attempted to analyze the consequence of cutting parameters on tool life and surface quality. The experimental work is consist of turning Al/SiCp (45%SiCp) weight with uncoated Carbide tools and the effect of three machining parameters including depth of cut, feed, and speed. Tool life and surface roughness have considered as process response for investigation. The predictive model has been developing to optimize the machining parameters in accordance to Box-Behnken design in Minitab 17, the contour plots the surface plot and response optimizer have made to study the influence of machining parameters and their interactions. ANOVA was carried out to identify the key factor affecting the tool life and surface roughness. The maximum tool life is 10.511 (min) and least surface roughness was observed 0.044 mu m. The abrasion and adhesive have the principle wear mechanism observed in machining process. Response surface methodology (RSM) approach have used to optimize the machining parameters, and the RSM model found more than 95% confidence level.

关键词： Al/SiCp metal matrix composite Tool life Surface roughness ANOVA Response surface methodology modeling and optimization

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modeling and optimization of turbidity removal from produced water using response surface methodology and artificial neural network

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South African Journal of Chemical Engineering 2021年 35卷 78-88页

作者： Ezemagu, I.G. Ejimofor, M.I. Menkiti, M.C. Nwobi-Okoye, C.C. Department of Chemical Engineering Nnamdi Azikiwe University Awka Anambra State Nigeria Water Resources Centre Texas Tech University Lubbock TX United States Department of Mechanical Engineering Chukwuemeka Odumegwu Ojukwu University Uli Anambra State Nigeria

In this study, results of parametric effects and optimization of turbidity removal from produced water using response surface methodology (RSM) and artificial neural network (ANN) based on a statistically designed experimentation via the Box–Behnken design (BBD) are reported. A three-level, three-factor BBD was employed using dosage (x₁), time (x₂) and temperature (x₃) as process variables. A quadratic polynomial model was obtained to predict turbidity removal efficiency. The RSM model predicted an optimal turbidity removal efficiency of 83% at conditions of x₁ (1 g/L), x₂ (16.5 min) and x₃ (45 °C) and validated experimentally as 82.73% with low model lack of fit F value of 0.6 and CV value of 8.22%. The ANN model predicted optimal turbidity removal of 83.01% at conditions of x₁ (1 g/L), x₂ (16.5 min) and x₃ (45 °C) and validated as 82.98%. Both models showed to be effective in describing the parametric effect of the considered operating variables on the turbidity removal from produced water. However, the ANN described the parametric effect more accurately when compared with the RSM model, with a smaller PRE (percentage relative error) and AAD (absolute average deviation) of ±0.0241% and ±0.0139%, respectively. © 2020 The Author(s)

关键词： ANN Box–Behnken design Coagulation modeling and optimization RSM Turbidity removal

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Knowledge expression,numerical modeling and optimization application of ethylene thermal cracking:From the perspective of intelligent manufacturing

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Chinese Journal of Chemical Engineering 2021年第10期34卷 1-17页

作者： Kexin Bi Shuyuan Zhang Chen Zhang Haoran Li Xinye Huang Haoyu Liu Tong Qiu Beijing Key Laboratory of Industrial Big Data System and Application Department of Chemical EngineeringTsinghua UniversityBeijing 100084China

Applications of process systems engineering(PSE)in plants and enterprises are boosting industrial reform from automation to digitization and *** ethylene thermal cracking,knowledge expression,numerical modeling and intelligent optimization are key steps for intelligent *** paper provides an overview of progress and contributions to the PSE-aided production of thermal cracking;introduces the frameworks,methods and algorithms that have been proposed over the past10 years and discusses the advantages,limitations and applications in industrial *** entire set of molecular-level modeling approaches from feedstocks to products,including feedstock molecular reconstruction,reaction-network auto-generation and cracking unit simulation are *** control and optimization methods are exhibited,including at the operational,cycle,plant and enterprise *** software packages are ***,an outlook in terms of future directions is presented.

关键词： Ethylene thermal cracking PSE Intelligent manufacturing Molecularization and digitization modeling and optimization

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Degradation of Fluoxetine using catalytic ozonation in aqueous media in the presence of nano-γ-alumina catalyst: Experimental, modeling and optimization study

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SEPARATION AND PURIFICATION TECHNOLOGY 2019年 211卷 551-563页

作者： Aghaeinejad-Meybodi, Abbas Ebadi, Amanollah Shafiei, Sirous Khataee, Alireza Kiadehi, Afshin Dehghani Urmia Univ Dept Chem Engn Orumiyeh Iran Sahand Univ Technol Fac Chem Engn EERC Tabriz Iran Univ Tabriz Dept Appl Chem Fac Chem Res Lab Adv Water & Wastewater Treatment Proc Tabriz Iran Babol Noshirvani Univ Technol Dept Chem Engn Babol Sar Iran

Degradation of Fluoxetine antidepressant by Catalytic ozonation in aqueous medium was investigated using nano-gamma-alumina catalyst. Catalyst was synthesized via co-precipitation method and was characterized by XRD, FESEM, FTIR and BET Techniques. Controlled precipitation helped to successfully prepare nano-sized gamma-alumina particles using sodium carbonate as the precipitating agent and aluminum nitrate as the precursor. Artificial neural network (ANN) and central composite design (CCD) were used to model and optimize degradation of Fluoxetine and results of the two models were compared. Furthermore, impacts of the basic operational variable, i.e. inlet ozone concentration, initial Fluoxetine concentration, nano-gamma-alumina dosage and reaction time, were studied. Back-propagation (BP) learning for three-layer feed-forward ANN with topology 4:8:1 and trainscg algorithm was used for development of the ANN model. A considerable agreement was observed between the values predicted by the ANN and CCD models for removal of Fluoxetine and the experimental results. Findings declared superiority of ANNs in describing nonlinear behavior of the catalytic process and accuracy of the ANN model in predicting the efficiency values of Fluoxetine elimination. Pareto analysis demonstrated effectiveness of the all selected factors on efficiency of removal. Results showed that the most effective variable in catalytic ozonation of Fluoxetine is reaction time with 44.97% percentage effect. Maximum removal efficiency of 96.14% was obtained for 30 mg L-1 inlet ozone concentration, 1 g L-1 nano-gamma-alumina catalyst dosage, 30 min reaction time and 28.56 mg L-1 initial Fluoxetine concentration in optimum conditions.

关键词： Catalytic ozonation Fluoxetine Artificial neural networks Central composite design modeling and optimization

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An Efficient Hybrid Sampling Method for Neural Network-Based Microwave Component modeling and optimization

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IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS 2020年第7期30卷 625-628页

作者： Zhang, Zhen Cheng, Qingsha S. Chen, Hongcai Jiang, Fan Harbin Inst Technol Harbin 150001 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Univ Key Lab Adv Wireless Commun Guangdong Prov Shenzhen 518055 Peoples R China

In this letter, we propose an efficient hybrid sampling method for microwave component modeling and optimization. The sampling method adaptively chooses samples from global and local samples to form a data set. The local samples are obtained using a greedy-like sampling method to exploit potential optimal solutions. The global samples are chosen using random sampling with minimum distance rejection to ensure the uniformity of the samples in the design space. The obtained data set is used to establish a surrogate model using the artificial neural networks (ANNs), and the optimal design parameters are obtained by optimizing the ANN model. A bandstop microstrip filter is taken as an example to verify the performance of the sampling method. The results show that the ANN model based on the proposed method achieves better modeling performance and yields better optimal design than the ANN model based on conventional sampling methods.

关键词： Sampling methods Microwave theory and techniques optimization Computational modeling Adaptation models Neural networks Testing Artificial neural networks (ANNs) microwave component modeling and optimization sampling method

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