scholarly journals Bioenergy II: Modeling and Multi-Objective Optimization of Different Biodiesel Production Processes

2010 ◽  
Vol 8 (1) ◽  
Author(s):  
Giovanni Di Nicola ◽  
Matteo Moglie ◽  
Marco Pacetti ◽  
Giulio Santori
Keyword(s):  
Energy Consumption ◽  
Production Process ◽  
Minimum Energy ◽  
Biodiesel Production ◽  
High Price ◽  
Optimization Analysis ◽  
Trade Off ◽  
Multi Objective ◽  
Optimal Configurations ◽  
Pareto Frontiers

One of the most promising renewable fuels proposed as an alternative to fossil fuels is biodiesel. The competitive potential of biodiesel is limited by the price of vegetable oils, which strongly influences the final price of biofuels. An appropriate planning and design of the whole production process, from the seed to the biodiesel end product, is essential in order to contain the fallout of energy inefficiencies in the high price of the end product. This study focuses on the characteristics of the production process currently used to produce biodiesel.Refined vegetable oil can be converted into biodiesel by means of a great variety of techniques and technologies, many of which are still not suitable for application on an industrial scale. The solution of greatest interest is homogeneous alkaline transesterification with KOH and methanol. Even when dealing with this type of conversion, it is impossible to establish a universal pattern to describe the conversion or purification stages because there are various possible solutions that make each system different from another. When we look more closely at the state of the art in industrial biodiesel production plants, we also encounter the potential problems introduced by the type and characteristics of the raw materials.Comparing some of the reference solutions that have inspired numerous installations, an optimization analysis was conducted using ASPENPLUS 2006, for the modeling of the process, and modeFRONTIER 4.1 for the optimization procedure. The optimization analysis was carried out using a multi-objective genetic algorithm optimization in order to define the configuration of the main parameters that guarantee the best trade-off between the maximization of the purity of some important compounds and the minimization of energy requirements in the process. The results of this analysis were Pareto frontiers that identify a family of configurations which define the best trade-off between the objectives. Using the Pareto frontiers we then selected the configuration that requires the minimum energy consumption. Among these optimal configurations there is one which guarantees the lowest specific energy consumption while all the optimal configurations obtained respected the requirements of EN 14214, in terms of biodiesel quality.

Multi-objective optimization of a biodiesel production process using process simulation

2020 ◽  
Vol 270 ◽  
pp. 122322
Author(s):  
Paula Ciribeli Gonçalves ◽  
Luciane Pimentel Costa Monteiro ◽  
Lizandro de Sousa Santos
Keyword(s):  
Production Process ◽  
Process Simulation ◽  
Biodiesel Production ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Increasing Service Life and System Efficiency of Parallel Pumps Using Combined Pump Regulation

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1808
Author(s):  
Safarbek Oshurbekov ◽  
Vadim Kazakbaev ◽  
Vladimir Prakht ◽  
Vladimir Dmitrievskii
Keyword(s):  
Energy Consumption ◽  
Service Life ◽  
Power Consumption ◽  
Minimum Energy ◽  
Trade Off ◽  
Pumping System ◽  
Minimum Energy Consumption ◽  
Combined Control ◽  
And Control ◽  
Maximum Reliability

The paper discusses the use of the combined control for a system of two parallel pumps to increase its service life. Using the combined control, the pumping system is controlled together by change the speed, throttling, and bypass. The power consumption of the pumping system is calculated for three methods of flow control: with minimum energy consumption, with maximum reliability, and control with a trade-off between efficiency and reliability. In the case of control with maximum reliability, the energy consumption of the pumping system is higher than in the case of control with minimum energy consumption by 29.2%. In the case of the proposed trade-off control, which provides acceptable reliability, the power consumption is higher than with the minimum energy consumption control by only 7.3%.

scholarly journals Developing a Resource Allocation Approach for Resource-Constrained Construction Operation under Multi-Objective Operation

2021 ◽  
Vol 13 (13) ◽  
pp. 7318
Author(s):  
Wei He ◽  
Wenjing Li ◽  
Wei Wang
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Limited Resource ◽  
Industrial Plant ◽  
Time Cost ◽  
Multi Objective Optimization ◽  
Trade Off ◽  
Quantum Genetic Algorithm ◽  
Multi Objective ◽  
Arrangement Problem

In the construction industry, it is of great importance for project managers (PM) to consider the resource allocation arrangement problem based on different perspectives. In this situation, the management of resources in construction becomes a challenge. Generally speaking, there are many objectives that need to be optimized in construction that are in conflict with each other, including time, cost, and energy consumption (EC). This paper proposed a multi-objective optimization framework based on the quantum genetic algorithm (QGA) to obtain the best trade-off relationship among these goals. The construction resources allocated in each construction activity would eventually determine its execution time, cost, and EC, and a complexed time-cost-energy consumption trade-off framework of the project is finally generated due to correlations between construction activities. QGA was performed to find the best combination among time, cost, and EC and the optimal scheme of resource arrangement under this state. The construction process is simulated in BIM to check the rationality of this resource allocation mode. An industrial plant office building in China is presented as an example to illustrate the implementation of the proposed model. The results show that the presented method could effectively reduce 7% of cost, 17% of time, and 21% of energy consumption. This developed model is expected to help PMs to solve the problem of multi-objective optimization with limited resource allocation.

scholarly journals Biodiesel production from microalgae by enzymatic transesterification

2015 ◽  
Author(s):  
◽  
Abhishek Guldhe
Keyword(s):  
Energy Consumption ◽  
Production Process ◽  
Lipid Extraction ◽  
International Standards ◽  
Biodiesel Production ◽  
Enzymatic Conversion ◽  
Microalgal Biomass ◽  
Whole Cell ◽  
Immobilized Lipases ◽  
Microalgal Lipids

Main focus of this study is to investigate the enzymatic-conversion of microalgal lipids to biodiesel. However, preceding steps before conversion such as drying of microalgal biomass and extraction of lipids were also studied. Downstream processing of microalgae has several challenges and there is very little literature available in this area. S. obliquus was grown in the pilot scale open pond cultivation system for biomass production. Different techniques were studied for biomass drying and extraction of lipids from harvested microalgal biomass. Effect of these drying and extraction techniques on lipid yield and quality was assessed. Energy consumption and economic evaluation was also studied. Enzymatic conversion of microalgal lipids by extracellular and whole cell lipase application was investigated. For both applications, free and immobilized lipases from different sources were screened and selected based on biodiesel conversion. Process parameters were optimized using chosen extracellular and whole cell lipases; also step-wise methanol addition was studied to improve the biodiesel conversion. Immobilized lipase was studied for its reuse. Final biodiesel was characterized for its fuel properties and compared with the specifications given by international standards. Enzymatic conversion of microalgal lipids was compared with the conventional homogeneous acid-catalyzed conversion. Enzymatic conversion and chemical conversion were techno-economically investigated based on process cost, energy consumption and processing steps. Freeze drying was the most efficient technique, however at large scale economical sun drying could also be selected as possible drying step. Microwave assisted lipid extraction performed better compared to sonication technique. Immobilized P. fluorescens lipase in extracellular application and A. niger lipase in whole cell application showed superior biodiesel conversion. The extracellular immobilized P. fluorescens lipase showed better biodiesel conversion and yields than the immobilized A. niger whole cell lipase. Both the enzyme catalysts showed lower biodiesel conversion compared to conventional chemical catalyst and higher processing cost. However, techno-economic analysis showed that, the reuse potential of immobilized lipases can significantly improve the economics. Fewer purification steps, less wastewater generation and minimal energy input are the benefits of enzymatic route of biodiesel conversion. Microalgae as a feedstock and lipase as a catalyst for conversion makes overall biodiesel production process environmentally-friendly. Data from this study has academic as well as industrial significance. Conclusions from this study form the basis for greener and sustainable scaling-up of microalgal biodiesel production process.

scholarly journals Multi-objective Optimization of Grid Computing for Performance, Energy and Cost

2017 ◽  
Vol 2 (3) ◽  
pp. 74-79
Author(s):  
Ahmed Badri Muslim Fanfakhri ◽  
Ali Yakoob Yousif ◽  
Esraa Alwan
Keyword(s):  
Energy Consumption ◽  
Message Passing ◽  
Minimum Energy ◽  
Minimum Cost ◽  
Parallel Applications ◽  
Jacobi Method ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Minimum Energy Consumption ◽  
Parallel Iterative

In this paper, new multi-objective optimization algorithm is proposed. It optimizes the execution time, the energy consumption and the cost of booked nodes in the grid architecture at the same time. The proposed algorithm selects the best frequencies depends on a new optimization function that optimized these three objectives, while giving equivalent trade-off for each one. Dynamic voltage and frequency scaling (DVFS) is used to reduce the energy consumption of the message passing parallel iterative method executed over grid. DVFS is also reduced the computing power of each processor executing the parallel applications. Therefore, the performance of these applications is decreased and so on the payed cost for the booking nodes is increased.  However, the proposed multi-objective algorithm gives the minimum energy consumption and minimum cost with maximum performance at the same time. The proposed algorithm is evaluated on the SimGrid/SMPI simulator while running the parallel iterative Jacobi method. The experiments show that it reduces on average the energy consumption by up to 19.7 %, while limiting the performance and cost degradations to 3.2 % and 5.2 % respectively.

scholarly journals Multi-objective optimization for oil-gas production process based on compensation model of comprehensive energy consumption using improved evolutionary algorithm

2020 ◽  
pp. 014459872097663
Author(s):  
Tan Liu ◽  
Qinyun Yuan ◽  
Lina Wang ◽  
Yonggang Wang ◽  
Nannan Zhang
Keyword(s):  
Energy Consumption ◽  
Production Process ◽  
Optimization Model ◽  
Error Compensation ◽  
Gas Production ◽  
Error Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Mechanism Model ◽  
Oil Gas

This paper establishes an error compensation multi-objective optimization model of oil-gas production process for optimizing these production indices, including overall oil production, overall water production and comprehensive energy consumption per ton of oil. In order to reduce the error between the model output and the actual value of comprehensive energy consumption per ton of oil, combining the mechanism model with least squares support vector machine (LS-SVM) error model optimized by Bayesian optimization algorithm (BOA), a hybrid model is established to predict the comprehensive energy consumption, in which the mechanism model is used to describe the overall characteristics of oil-gas production process, and LS-SVM error model is established to compensate the mechanism model error. Then, in order to improve the performance of Pareto non-dominated solutions, an improved non-dominated sorting genetic algorithm-II with multi-strategy improvement (IMS-NSGA-II) is proposed to solve the error compensation multi-objective optimization model. Finally, the effectiveness and superiority of the the proposed optimization method are verified by the experiment results on some stand test problems and the optimization problem for the oil-gas production process in a block of an oil production operation area.

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