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An artificial intelligence approach to model and optimize biodiesel production from waste cooking oil using life cycle assessment and market dynamics analysis

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ENERGY 2024年 307卷

作者： Corral-Bobadilla, Marina Lostado-Lorza, Ruben Sabando-Fraile, Celia Iniguez-Macedo, Saul Univ La Rioja Dept Mech Engn C Jose Calasanz 31 Logrono 26004 La Rioja Spain Footwear Technol Ctr La Rioja CTCR C Raposal 65 Arnedo 26580 La Rioja Spain

Biodiesel has emerged as a viable alternative to fuel, offering a more sustainable and environmentally friendly energy option. The current study explores the modeling and optimization of biodiesel production from waste cooking oil using artificial intelligence and genetic algorithms. The study focuses on enhancing five process parameters: methanol-to-oil molar ratio, catalyst concentration, reaction temperature, reaction time, and stirring speed. The optimization of these parameters is complemented by a life cycle assessment to reduce environmental impact. The approach considers biodiesel yield, high heating value, and energy consumption as output variables, thereby advancing sustainable biodiesel production. The findings indicated that, under optimal conditions (methanol-to-oil ratio of 1:6.9, stirring rate of 500 rpm, reaction duration of 20 s, reaction temperature of 30 degrees C and catalyst concentration of 1), the transesterification process achieved the maximum biodiesel yield of 97.76 %. The optimization reached a low environmental impact in the production of biodiesel in an efficient way. Additionally, SWOT analysis helps to develop strategic methods that can enhance efficiency and increase competitiveness. The research suggests that, by optimizing the chemical process in biodiesel production, it is possible to achieve a high yield and high heating value of the biofuel, along with feasible environmental mitigation strategies.

关键词： Biodiesel Eco-efficiency Waste reduction genetic algorithms optimization Life cycle assessment Market dynamics analysis

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BIM-based approach for optimizing life cycle costs of sustainable buildings

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JOURNAL OF CLEANER PRODUCTION 2018年 188卷 217-226页

作者： Marzouk, Mohamed Azab, Shimaa Metawie, Mahmoud Cairo Univ Fac Engn Struct Engn Dept Giza Egypt Inst Natl Planning Environm Planning & Dev Ctr Environm Planning Dept Cairo 11765 Egypt Cairo Univ Fac Engn Struct Engn Dept Giza 12613 Egypt

This paper proposes a framework that integrates Building Information Modeling (BIM) with genetic Algorithm optimization and Monte-Carlo Simulation. The developed framework helps in implementing a stochastic Life-Cycle Cost (LCC) model for building to select the optimum building materials alternatives and discover the most influential building system in each cost element starting from initial cost to end on life cost. genetic algorithms optimization technique is utilized to select the optimum alternatives of building systems taking in consideration sustainability aspects. The Monte-Carlo Simulation model is used as a fitness function for the optimization model. The environmental aspect of building is achieved by considering the maximum number of points that can be awarded under the Leadership in Energy and Environmental Design (LEED) rating system. Sensitivity analysis is performed on the optimum solutions that are chosen by optimization a model to examine the effect of different building systems on LCC and its components through computing the rank order of building systems and the target output. A case study is presented to demonstrate the practical features of the proposed framework. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Green-building materials Life-cycle cost (LCC) Monte-carlo simulation genetic algorithms optimization Building information modeling (BIM)

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LaPS: LiDAR-assisted Placement of Wireless Sensor Networks in Forests

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ACM TRANSACTIONS ON SENSOR NETWORKS 2019年第2期15卷 17-17页

作者： Demetri, Silvia Picco, Gian Pietro Bruzzone, Lorenzo Univ Trento Dept Informat Engn & Comp Sci DISI Via Sommarive 9 I-38123 Trento Italy

The deployment of a wireless sensor network (WSN) is crucial to its reliability and performance. Yet, node placement is typically determined in-field via effort-demanding trial-and-error procedures, because existing approaches over-simplify the radio environment;this especially holds for forests, the focus of this article, where trees greatly affect communication. We present LaPS (LiDAR-assisted Placement for wireless Sensor networks), an approach exploiting remote sensing to identify the best node placement automatically and prior to deployment. Airborne Light Detection and Ranging (LiDAR) data acquired for the target forest are automatically processed to estimate its properties (e.g., tree position and diameter) that, once incorporated into a specialized path loss model, enable per-link estimates of the radio signal attenuation induced by trees. Finally, a genetic algorithm explores placement options by evolving toward a (sub-)optimal solution while satisfying the user's spatial and network requirements, whose formulation is very flexible and broadly applicable. Our experiments, focused on a real forest, confirm that LaPS yields topologies of significantly higher quality w.r.t. approaches using a regular placement or a standard path loss model. Further, the ability to quickly explore the impact that changes in user requirements have on topology is invaluable to improve the operation of WSNs and reduce the effort of their in-field deployment.

关键词： Wireless sensor networks low-power wireless communication remote sensing LiDAR genetic algorithms optimization

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optimization of the Thermal Performance of the Solar Water Heater (SWH) Using Stochastic Technique

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2018年第3期8卷 1401-1410页

作者： Badr, Ouhammou Mohammed, Aggour Brahim, Daouchi Ibn Tofail Univ Fac Sci Dept Phys Renewable Energy & Environm Lab BP 133 Kenitra 14000 Morocco

The efficiency of Solar Water Collector (SWH) is low and in order to increase its thermal performances, various optimization techniques were used. In this paper, a stochastic method (genetic Algorithm (GA)) was adopted to increase the efficiency of the active SWH under various climatic conditions and for various operating parameters. The optimization model was introducing a lot of objectives in order to evaluate the optimality of the SWH. For the dynamic Reynolds number, solar radiation intensity and ambient temperature, different values of plate emissivity, a different number of the glass cover and velocity of air, the thermal performance were obtained and it is compared with the experimental results. It is established by the studies carried out based upon this algorithm that the maximum thermal efficiency comes out to be 75, 28 %. The application of our results was applied in such application as coupling the SWH with the anaerobic digester. The goal is to support decision makers in the definition of the optimal thermal performance and of the optimal collector flat area in order to give a good compromise between the collector efficiency and the output water temperature in the country.

关键词： genetic algorithms optimization Thermal performance Solar water heater Thermal solar application

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Optimal Design of Circular Antenna Array Using genetic algorithms 9

Optimal Design of Circular Antenna Array Using Genetic Algor...

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9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Enache, Florin Departcanu, Daniel Popescu, Florin Mil Tech Acad Commun & Mil Elect Syst Dept Bucharest Romania

ISBN: (纸本)9781509064588

This paper presents the design of circular antenna array with isotropic radiators by optimization using genetic algorithms. The problem consists in the obtaining of an imposed radiation pattern starting from a uniform circular antenna array. The goal is achieved using non-uniform distances between the circles which the radiators are disposed on and a non-uniform distribution of currents through the radiators on the successive circles. The work results and the paper conclusions reveal advantage of our approach versus the uniform circular antenna array. Also an important result is the reduction of the number of variables in the fitness function, which creates good premises for the implementation of real-time algorithms useful in complex/dynamic adaptive arrays.

关键词： circular antenna array non-uniform currents distribution genetic algorithms optimization sidelobe level beamwidth

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Speed Optimal-setting of Electric Multiple Units Based on Energy-efficient Operation

Speed Optimal-setting of Electric Multiple Units Based on En...

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2015 IEEE Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2015)

作者： ZHANG Qiongjie ZHANG Tiezhu School of Vehicle Engineering Zhengzhou Railway Vocational and Technical College

ISBN: (纸本)9781479919819

The operation process of electric multiple units(EMUs) is characterized by complex running environment,and frequent change of operating *** is difficult to accomplish energy-efficient operation using the given V-S curve(velocity versus position curve).And the alternation of coasting points will have different running time and energy consumption,then appropriate coasting scheme is an effective energy-saving *** on the traction energy consumption and running time model,we exploit a data-driven RBF(radial basis function)neural network to build a model that connects the coasting points and their corresponding energy consumption and running ***,we adopt GA(genetic algorithms) to optimize the outputs of the RBF,and the speed optimal-setting profile satisfying punctual,energy-efficient operation is *** results show the effectiveness of this method.

关键词： electric multiple units energy consumption coasting points RBF genetic algorithms optimization

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Fractional adaptive control for an automatic voltage regulator

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ISA TRANSACTIONS 2013年第6期52卷 807-815页

作者： Aguila-Camacho, Norelys Duarte-Mermoud, Manuel A. Univ Chile Dept Elect Engn Santiago Chile Adv Min Technol Ctr Santiago Chile

This paper presents the application of a direct Fractional Order Model Reference Adaptive Controller (FOMRAC) to an Automatic Voltage Regulator (AVR). A direct FOMRAC is a direct Model Reference Adaptive Control (MRAC), whose controller parameters are adjusted using fractional order differential equations. Four realizations of the FOMRAC were designed in this work, each one considering different orders for the plant model. The design procedure consisted of determining the optimal values of the fractional order and the adaptive gains for each adaptive law, using genetic algorithm optimization. Comparisons were made among the four FOMRAC designs, a fractional order PID (FOPID), a classical PID, and four Integer Order Model Reference Adaptive Controllers (IOMRAC), showing that the FOMRAC can improve the controlled system behavior and its robustness with respect to model uncertainties. Finally, some performance indices are presented here for the controlled schemes, in order to show the advantages and disadvantages of the FOMRAC. (C) 2013 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Fractional order adaptive control Model reference adaptive control Automatic voltage regulator genetic algorithms optimization

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A multiobjective optimization of a UWB antenna using a self organizing genetic algorithm

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2012年第8期54卷 1824-1828页

作者： Silva, C. R. M. Lins, H. W. C. Martins, S. R. Barreto, E. L. F. d'Assuncao, A. G. Fed Univ Rio Grande do Norte UFRN Dept Commun Engn BR-59072970 Natal RN Brazil

This article presents the optimization process of a microstrip antenna designed to operate with ultra-wideband applications. This optimization process was accomplished by applying a self organizing multiobjective genetic algorithm (GA) to optimize a geometry aspect of a ring monopole antenna (a slit in the ground plane) considering three objectives simultaneously (bandwidth, return loss and central frequency deviation). Each of these objectives are modeled as dependent of two variables (the slit dimensions) and used in a single weighted compound aggregate objective function in which weights are defined by an adjuster GA that uses another GA to evaluate each combination of possible weights. The results were compared with the ones obtained to the same antenna using a simulator program (Ansoft HFSS) and with the results of a real prototype antenna built from the slit dimension optimal values obtained after the optimization process. (C) 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:18241828, 2012;View this article online at ***. DOI 10.1002/mop.26945

关键词： microstrip antennas multiobjective evolutionary algorithms genetic algorithms optimization

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Distributed Unequal Error Protection Rateless Codes over Erasure Channels: A Two-Source Scenario

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IEEE TRANSACTIONS ON COMMUNICATIONS 2012年第8期60卷 2084-2090页

作者： Talari, Ali Rahnavard, Nazanin Oklahoma State Univ Dept Elect & Comp Engn Stillwater OK 74078 USA

In distributed rateless coding, multiple disjoint sources need to deliver their rateless coded symbols (where a symbol may contain a single bit or thousands of bits) to a common destination via a single relay. In this paper, we propose and design novel distributed rateless codes called DU-rateless codes that can provide Unequal Error Protection (UEP) for disjoint sources with unequal data lengths on erasure channels. To design DU-rateless codes, we tune the coding parameters at each source and propose to smartly combine the encoded symbols at the relay. We analyze DU-rateless codes employing And-Or tree analysis technique and leverage our analysis to design several sets of codes for various setups employing the-state-of-the-art multi-objective genetic algorithms. We evaluate the performance of the designed codes using numerical simulations and discuss their advantages.

关键词： Distributed rateless codes genetic algorithms optimization unequal error protection erasure channels

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Solving optimization Problems in Nimrod/OK using a genetic Algorithm

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Procedia Computer Science 2012年 9卷 1647-1656页

作者： Yu Hua Lim Jefferson Tana David Abramsonb Monash University Clayton 3800 Victoria Australia

A scientific workflow can be viewed as formal model of the flow of data between processing components. It often involves a combination of data integration, computation, analysis, and visualization steps. An emerging use case involves determining some input parameters that minimize (or maximize) the output of a computation. Kepler is a good framework for specifying such optimizations because arbitrary computations can be composed into a pipeline, which is then repeated until an optimal set of inputs is found. genetic algorithms are generic optimization algorithms based on the principles of genetics and natural selection, and are well suited for models with discontinuous objective functions. This paper discusses an implementation of a genetic Algorithm in Kepler, building on the Nimrod/OK framework. The resulting tool is generic and flexible enough to support a variety of experimental domains. The paper reports a number of experiments that demonstrate the performance with a set of benchmarking functions.

关键词： Scientific Worklows Kepler genetic algorithms optimization

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