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Maximised bioethanol extraction from bamboo biomass through alkali pretreatment and enzymatic saccharification by application of ANN-NSGA-ii-based optimisation method

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2023年 1-23页

作者： Kiran, K. Jose, Nirmal Rout, Amruta Ravikumar, R. Hebbar, Gurumoorthy S. CHRIST Univ Sch Engn & Technol Mech & Automobile Engn Dept Bengaluru 560074 Karnataka India

The demand for alternative fuels is growing due to the depletion of fossil fuel resources. non-edible resources are explored as alternatives, and a bamboo is an up-and-coming option for producing ethanol. The extraction process for bioethanol from bamboo involves alkali pretreatment, enzymatic saccharification, and ethanol production. The bamboo biomass is treated with alkali at high temperatures and pressure. This treatment helps break the lignin bonds that hinder the reaction between cellulose and enzymes. As a result, the pretreated biomass contains 40% less lignin than its raw form. Next, the air-dried pretreated biomass undergoes saccharification using Supercut Acid Cellulose. The saccharification process is optimised to achieve the shortest possible time, determined through prediction models based on artificial neural networks and optimisation techniques like non-dominated sorting genetic algorithm-ii. The optimised process involves specific biomass and enzyme loading, producing reducing sugars estimated using the DNS method. Following enzymatic Saccharification, the hydrolysate is fermented using Saccharomyces cerevisiae, a type of yeast. This fermentation process yields ethanol with a 1614.26 mg/kg concentration.

关键词： Bamboo Saccharification Bioethanol Artificial neural network non-dominated sorting genetic algorithm-ii

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NSGA-ii based optimal control scheme of wind thermal power system for improvement of frequency regulation characteristics

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AIN SHAMS ENGINEERING JOURNAL 2015年第3期6卷 851-863页

作者： Chaine, S. Tripathy, M. Satpathy, S. Veer Surendra Sai Univ Technol Dept Elect Engn Burla 768018 Odisha India

This work presents a methodology to optimize the controller parameters of doubly fed induction generator modeled for frequency regulation in interconnected two-area wind power integrated thermal power system. The gains of integral controller of automatic generation control loop and the proportional and derivative controllers of doubly fed induction generator inertial control loop are optimized in a coordinated manner by employing the multi-objective non-dominated sorting genetic algorithm-ii. To reduce the numbers of optimization parameters, a sensitivity analysis is done to determine that the above mentioned three controller parameters are the most sensitive among the rest others. non-dominated sorting genetic algorithm-ii has depicted better efficiency of optimization compared to the linear programming, genetic algorithm, particle swarm optimization, and cuckoo search algorithm. The performance of the designed optimal controller exhibits robust performance even with the variation in penetration levels of wind energy, disturbances, parameter and operating conditions in the system. (C) 2015 Production and hosting by Elsevier B.V. on behalf of Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Doubly fed induction generator Frequency regulation AGC Wind energy conversion systems non-dominated sorting genetic algorithm-ii

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Proposing a novel theoretical optimized model for the combined dry and steam reforming of methane in the packed-bed reactors

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CHEMICAL PAPERS 2019年第9期73卷 2309-2328页

作者： Lotfi, Naser Ebrahim, Habib Ale Azarhoosh, Mohammad Javad Amirkabir Univ Technol Chem Engn Dept Petrochem Ctr Excellence Tehran Polytech Tehran 158754413 Iran Urmia Univ Chem Engn Dept Fac Engn Orumiyeh Iran

A packed-bed reactor was modeled for combined dry and steam reforming (CDSR) and simulated using a two-dimensional heterogeneous model at steady-state condition. The model outputs showed a good agreement with experimental data. The effects of important operational parameters such as feed temperature, pressure, molar flow, and CO2/CH4 and H2O/CH4 ratios on methane conversion and H-2/CO ratio in synthesis gas were also evaluated. Afterward, the modified artificial neural network (ANN) model was used for approximating the results of a simulation with high accuracy. The outputs of ANN model show that the predicted values of ANN model are in good agreement with those of the heterogeneous model, suggesting that the model was successfully developed to capture the correlation between operation conditions, methane conversion, and H-2/CO ratio in the synthesis gas. Finally, a multi-objective optimization based on the hybrid of ANN and non-dominated sorting genetic algorithm-ii (NSGA-ii) was carried out to find the best-operating conditions for the methanol production and Fischer-Tropsch synthesis reaction with the desired H-2/CO molar ratio of about two in synthesis gas. So, the main objectives for CDSR are providing a high methane conversion and also H-2/CO ratio of two in the output synthesis gas.

关键词： Combined steam and dry reforming Fixed-bed reactor Heterogeneous model Artificial neural networks non-dominated sorting genetic algorithm-ii

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A novel multi-objective evolutionary algorithm for shortest path routing problem

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INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS 2011年第3-4期7卷 355-374页

作者： Chitra, C. Subbaraj, P. Arulmigu Kalasalingam Coll Engn Fac Elect & Commun Engn Dept Elect & Commun Engn Krishnankoil 626190 Tamil Nadu India Theni Kammavar Sangam Coll Technol Theni 625534 Tamil Nadu India Thiagarajar Coll Engn Dept Elect & Elect Engn Madurai Tamil Nadu India

This paper presents an application of non-dominated sorting genetic algorithm-ii (NSGA-ii) technique for solving shortest path routing problems in computer networks. The problem is formulated as a non-linear constrained multi-objective optimisation problem. NSGA-ii is applied to handle shortest path routing problem as a true multi-objective optimisation problem (MOOP) with competing and non-commensurable objectives. A priority-based encoding scheme is employed for population initialisation. Priorities are assigned to all the edges and NSGA-ii is implemented to find the optimal solution. It is noted that this approach can find a diverse set of solutions and is converging near the true Pareto-optimal set. Results for a sample test network have been presented to demonstrate the capabilities of the NSGA-ii algorithm to generate well-distributed Pareto-optimal solutions of shortest path routing problem in one single run. The results obtained by NSGA-ii are compared with single objective weighting factor method for which genetic algorithm (GA) is applied.

关键词： shortest path routing non-dominated sorting genetic algorithm-ii communication network weighted sum method multi-objective optimisation

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Multi-objective optimisation of friction stir welding parameters: integration of FEM and NSGA-ii

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INTERNATIONAL JOURNAL OF MANUFACTURING RESEARCH 2021年第1期16卷 82-101页

作者： Rao, Thella Babu Rao, C. S. P. Baki, Naresh Natl Inst Technol Andhra Pradesh Dept Mech Engn Tadepalligudem 534101 Andhra Pradesh India Natl Inst Technol Andhra Pradesh Tadepalligudem 534101 Andhra Pradesh India GITAM Univ Sch Technol Dept Mech Engn Hyderabad 502329 India

In this investigation, the friction stir welding (FSW) parameters were optimised by integrating the finite element analysis and non-dominated sorting genetic algorithm-ii. A thermo-mechanical finite element model to predict the peak temperature and flow stresses during welding were developed using Hyper Weld (R) FEM tool. The validity of the FE model was validated through experimental results. Using the validated FE model the process responses were predicted for varying conditions of welding speed, tool rotational speed, tilt angle, and shoulder diameter according to the design of experiments. The quadratic models for the process responses were postulated using response surface methodology and their adequacy was verified through analysis of variance. The problem was treated as multi-response optimisation due to the conflicting nature of the process responses and formulated to maximise the peak temperature and minimise the flow stress simultaneously. NSGA-ii was then implemented to derive the Pareto-optimal solutions and reported.

关键词： friction stir welding FSW thermo-mechanical modelling peak temperature flow stress response surface methodology multi-response optimisation non-dominated sorting genetic algorithm-ii NSGA-ii

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A Multi-objective Multimodal Evolutionary algorithm Using a Novel Tournament and Environmental Selections

A Multi-objective Multimodal Evolutionary Algorithm Using a ...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Javadi, Mahrokh Mostaghim, Sanaz Otto von Guericke Univ Fac Comp Sci Magdeburg Germany

ISBN: (纸本)9781728190488

Multi-modal multi-objective optimization refers to multi-objective optimization problems that have more than one Pareto set. This paper proposes a new algorithm, modifying both the tournament and the environment selections. The purpose of this paper is to utilize the full potential of the parental population to produce diverse offspring and avoid producing duplicated solutions. In order to achieve the goal, we modify the selection mechanisms, which usually enforce the selection of solutions with high quality. This can lead to duplicates in the mating pool. Using the modified tournament selection, we aim to enable the algorithm to select diverse solutions in sparse regions of the search space. Our experiments agree with our expectations, confirming that our proposed algorithm is more effective than current competitor algorithms at approximating Pareto sets in most multimodal multi-objective optimization problems with different number of decision variables and objective functions.

关键词： Tournament Selection Multimodal Multi-objective Evolutionary algorithm non-dominated sorting genetic algorithm-ii Niching Techniques Mating Selection Environmental Selection

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A Stochastic Sequence-dependent Multi-objective Disassembly Line Balancing Model Subject to Task Failure and Resource Constraint via Multi-objective Cuckoo Search

A Stochastic Sequence-dependent Multi-objective Disassembly ...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Wang, TianYuan Guo, XiWang Liu, ShiXin Qi, Liang Zhao, ZiYan Liaoning Shihua Univ Coll Comp & Commun Engn Fushun 113001 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Shandong Univ Sci & Technol Dept Comp Intelligent Sci & Tech Qingdao 266590 Peoples R China

ISBN: (纸本)9781728185262

A Disassembly Line Balancing Problem (DLBP) exists in the remanufacturing of discarded products. It involves such factors as sequence-dependent among components, multi-resource constraints, limited number of workstations, uncertainty of disassembly time, and disassembly failure risk. Effective decisions can be made by taking them into full consideration. This work establishes a stochastic sequence-dependent multi-objective DLBP model subject to disassembly failure and resource constraints. Its objectives are maximization of profit and minimization of energy consumption. A multi-objective cuckoo search algorithm is proposed. Then, three real products are disassembled to verify the effectiveness and feasibility of the proposed approach. Experimental results show the superior of the proposed algorithm over multi-objective artificial bee colony algorithm and non-dominated sorting genetic algorithm ii.

关键词： Disassembly line balancing resource-constrained Metaheuristic algorithms Cuckoo Search Artificial Bee Colony non-dominated sorting genetic algorithm-ii

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Enhanced multi-objective solution approach for multiple quality characteristics optimisation problems considering predictive uncertainties

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INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT 2019年第1期37卷 112-144页

作者： Sharma, Abhinav Kumar Mukherjee, Indrajit Indian Inst Technol Shailesh J Mehta Sch Management Mumbai Maharashtra India

Purpose The purpose of this paper is to address three key objectives. The first is the proposal of an enhanced multiobjective optimisation (MOO) solution approach for the mean and mean-variance optimisation of multiple "quality characteristics" (or "responses"), considering predictive uncertainties. The second objective is comparing the solution qualities of the proposed approach with those of existing approaches. The third objective is the proposal of a modified non-dominated sorting genetic algorithm-ii (NSGA-ii), which improves the solution quality for multiple response optimisation (MRO) problems. Design/methodology/approach The proposed solution approach integrates empirical response surface (RS) models, a simultaneous prediction interval-based MOO iterative search, and the multi-criteria decision-making (MCDM) technique to select the best implementable efficient solutions. Findings Implementation of the proposed approach in varied MRO problems demonstrates a significant improvement in the solution quality in worst-case scenarios. Moreover, the results indicate that the solution quality of the modified NSGA-ii largely outperforms those of two existing MOO solution strategies. Originality/value No evidence exists of earlier research that has demonstrated the suitability and superiority of an MOO solution approach for both mean and mean-variance MRO problems, considering RS uncertainties. Furthermore, this work illustrates the step-by-step implementation results of the proposed approach for the six selected MRO problems.

关键词： Quality improvement Multiobjective optimization Multiple response optimization non-dominated sorting genetic algorithm-ii Prediction interval Response surface

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Cuckoo Search algorithm Based Optimal Tuning of PID Structured TCSC Controller 1

Cuckoo Search Algorithm Based Optimal Tuning of PID Structur...

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1st International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Sethi, Rupashree Panda, Sidhartha Sahoo, Bibhuti Prasad Veer Surendra Sai Univ Technol Dept Elect Engn Burla 768018 Odisha India

ISBN: (纸本)9788132222057;9788132222040

Cuckoo Search algorithm (CSA) is one of the new nature inspired meta-heuristic algorithms for solving many engineering optimization problems. This paper investigates the application of CSA technique for the tuning of proportional integral derivative (PID) structured Thyristor Controlled Series Compensator (TCSC) based controller to improve damping of the power system when subjected to different disturbances at various loading conditions. The dynamic performances of proposed approach are analyzed by taking an example of Single Machine Infinite Bus (SMIB) power system in MATLAB/Simulink environment. The superiority of the proposed approach is demonstrated by comparing the simulation results with previously published technique such as non-dominated sorting genetic algorithm-ii (NSGA-ii) based TCSC controller for the same power system.

关键词： Cuckoo search algorithm Thyristor controlled series compensator PID controller Power system stability Single-machine infinite-bus system non-dominated sorting genetic algorithm-ii

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Multi-Objective Location of Fresh Food E-Commerce Distribution Network Based on Improved NSGA-ii algorithm 6

Multi-Objective Location of Fresh Food E-Commerce Distributi...

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6th International Conference on Transportation Engineering (ICTE)

作者： Deng, Xinyue Zhang, Jin Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 610031 Sichuan Peoples R China Southwest Jiaotong Univ Natl United Engn Lab Integrated & Intelligent Tra Chengdu 610031 Sichuan Peoples R China Southwest Jiaotong Univ Natl & Reg Joint Engn Lab Comprehens Intelligent Chengdu 610031 Sichuan Peoples R China

ISBN: (纸本)9780784482742

To optimize the distribution network location of fresh food e-commerce, based on the traditional location model, customer satisfaction, and the loss function of fresh goods were defined, the multi-objective location model of the fresh food e-commerce distribution network was established to get the moderate balance between customer satisfaction and the cost of the distribution. Combined with multi-population optimization algorithms, an improved non-dominated sorting genetic algorithm-ii (NSGA-ii) was proposed. Multiple populations are introduced to search at the same time, and an elite population is formed to co-evolution, improving the population diversity and the search performance of global search and local search. The simulation results show that the improved NSGA-ii algorithm can effectively obtain the Pareto optimal solution of the model and provide a comprehensive decision for the distribution network location of the fresh food e-commerce enterprises.

关键词： fresh food e-commerce site selection problems multi-objective optimization non-dominated sorting genetic algorithm-ii

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