ANFIS modeling of biodiesels' physical and engine characteristics: A review

 Population increase has resulted in an increase in the worldwide demand for alternative fuels due to depleting resources. There is a periodic increase in concern about the engine performance, pollutant emissions, and their predictions, from an engine using biodiesels. The use of intelligent algorithms in modeling and forecasting alternative fuels characteristics and their performance in engines are critically reviewed in this study. The paper aims at demonstrating with artificial intelligence methodologies the main conclusions of the recent research done for the above topic from 2012 to 2020. This article attempted to demonstrate an exploratory examination of the adaptive neuro-fuzzy inference system (ANFIS) soft computing technique used for the exact measurement and analysis of engine performance, emissions of exhaust engines when biodiesel is used as an alternative fuel. Additionally, the yield of biodiesel and their different characteristics predicted using ANFIS are also reviewed. Integration of particle swarm optimization (PSO), genetic algorithm (GA), and response surface methodology (RSM), either for comparison or optimization with ANFIS is presented. The summary of all studies is provided in tabular form. For the demonstration purpose, the ANFIS studies predicting different biodiesel and engine characters are provided with illustrative figures. The ANFIS prediction related to biodiesel used engine and biodiesel self-characteristics is found to be excellent. The ANFIS accuracy reported is better than the artificial neural network (ANN) accuracy. A minimum of 0.9 R² value is generally obtained which is around 5% greater than the ANN modeling results reported. However, the ANFIS predictions are much more fitter than the RSM predictions. The integration of ANFIS-PSO and ANFIS-GA provided much more optimized results.     

Machine learning in energy economics and finance: A review

Machine learning (ML) is generating new opportunities for innovative research in energy economics and finance. We critically review the burgeoning literature dedicated to Energy Economics/Finance applications of ML. Our review identifies applications in areas such as predicting energy prices (e.g. crude oil, natural gas, and power), demand forecasting, risk management, trading strategies, data processing, and analyzing macro/energy trends. We critically review the content (methods and findings) of more than 130 articles published between 2005 and 2018. Our analysis suggests that Support Vector Machine (SVM), Artificial Neural Network (ANN), and Genetic Algorithms (GAs) are among the most popular techniques used in energy economics papers. We discuss the achievements and limitations of existing literature. The survey concludes by identifying current gaps and offering some suggestions for future research.     

Computational modeling of biodiesel production using supercritical methanol

Renewable fuels such as biodiesel are introduced as promising environmental friendly fuels and they can be applied as alternative fuels instead of fossil fuels. In the present study, a modeling study based on statistical learning theory was investigated by the least square support vector machine (LSSVM) approach for non-catalytic biodiesel production in supercritical methanol. This model can estimate the biodiesel yield as a function of temperature, pressure, reaction time, and Methanol/oil ratio. The results indicated that the suggested LSSVM model was a satisfactory model to predict biodiesel yield that was confirmed by a high value of R² (0.9961) and low value of absolute deviation (1.17%). In addition, our model has been compared with another previous Artificial neural network (ANN)-based model and great estimations of both models were proved.     

Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden

The development of a model for any energy system is required for proper design, operation or its monitoring. Models based on accurate mathematical expressions for physical processes are mostly useful to understand the actual operation of the plant. However, for large systems like combined heat and power (CHP) plants, such models are usually complex in nature. The estimation of output parameters using these physical models is generally time consuming, as these involve many iterative solutions. Moreover, the complete physical model for new equipment may not be available. However, artificial neural network (ANN) models, developed by training the network with data from an existing plant, may be very useful especially for systems for which the full physical model is yet to be developed. Also, such trained ANN models have a fast response with respect to corresponding physical models and are useful for real-time monitoring of the plant. In this paper, the development of an ANN model for the biomass and coal cofired CHP plant of Västhamnsverket at Helsingborg, Sweden has been reported. The feed forward with back propagation ANN model was trained with data from this plant. The developed model is found to quickly predict the performance of the plant with good accuracy.     

Artificial neural networks used for the prediction of the cetane number of biodiesel

Cetane number (CN) is one of the most significant properties to specify the ignition quality of any fuel for internal combustion engines. The CN of biodiesel varies widely in the range of 48–67 depending upon various parameters including the oil processing technology and climatic conditions where the feedstock (vegetable oil) is collected. Determination of the CN of a fuel by an experimental procedure is a tedious job for the upcoming biodiesel production industry. The fatty acid composition of base oil predominantly affects the CN of the biodiesel produced from it. This paper discusses the currently available CN estimation techniques and the necessity of accurate prediction of CN of biodiesel. Artificial Neural Network (ANN) models are developed to predict the CN of any biodiesel. The present paper deals with the application of multi-layer feed forward, radial base, generalized regression and recurrent network models for the prediction of CN. The fatty acid compositions of biodiesel and the experimental CNs are used to train the networks. The parameters that affect the development of the model are also discussed. ANN predicted CNs are found to be in agreement with the experimental CNs. Hence, the ANN models developed can be used reliably for the prediction of CN of biodiesel.     

Recent advances in artificial neural network research for modeling hydrogen production processes

Artificial Neural Networks (ANN) have been widely used by scientists in a variety of energy modes (biomass, wind, solar, geothermal, and hydroelectric). This review highlights the assistance of ANN for researchers in the quest for discovering more advanced materials/processes for efficient hydrogen production (HP). The review is divided into two parts in this context. The first section briefly mentions, in terms of technologies, economy, energy consumption, and costs symmetrically outlined the advantages and disadvantages of various HP routes such as fossil fuel/biomass conversion, water electrolysis, microbial fermentation, and photocatalysis. Subsequently, ANN and ANN hybrid studies implemented in HP research were evaluated. Finally, statistics of hybrid studies with ANN are given, and future research proposals and hot research topics are briefly discussed. This research, which touches upon the types of ANNs applied to HP methods and their comparison with other modeling techniques, has an essential place in its field.     

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

The new generation of artificial intelligence (AI), called AI 2.0, has recently become a research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and electric power system (Smart EEPS). In AI 2.0, machine learning (ML) forms a typical representative algorithm category used to achieve predictions and judgments by analyzing and learning from massive amounts of historical and synthetic data to help people make optimal decisions. ML has preliminarily been applied to the Smart Grid (SG) and Energy Internet (EI) fields, which are important Smart EEPS representatives. AI 2.0, especially ML, is undergoing a critical period of rapid development worldwide and will play an essential role in Smart EEPS. In this context, this study, combined with the emerging SG and EI technologies, takes the typical representative of AI 2.0—ML—as the research objective and reviews its research status in the operation, optimization, control, dispatching, and management of SG and EI. The paper focuses on introducing and summarizing the mainstream uses of seven representative ML methods, including reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, and ensemble learning, in the SG and EI fields. In this survey, we begin with an introduction to these seven types of ML methods and then systematically review their applications in Smart EEPS. Finally, we discuss ML development under the big data thinking and offer a prospect for the future development of AI 2.0 and ML in Smart EEPS. We conduct this survey intended to arouse the interest and excitement of experts and scholars in the EEPS industry and to look ahead to efforts that jointly promote the rapid development of AI 2.0 in the Smart EEPS field.     

Hybrid renewable energy systems for power generation in stand-alone applications: A review

It has become imperative for the power and energy engineers to look out for the renewable energy sources such as sun, wind, geothermal, ocean and biomass as sustainable, cost-effective and environment friendly alternatives for conventional energy sources. However, the non-availability of these renewable energy resources all the time throughout the year has led to research in the area of hybrid renewable energy systems. In the past few years, a lot of research has taken place in the design, optimization, operation and control of the renewable hybrid energy systems. It is indeed evident that this area is still emerging and vast in scope. The main aim of this paper is to review the research on the unit sizing, optimization, energy management and modeling of the hybrid renewable energy system components. Developments in research on modeling of hybrid energy resources (PV systems), backup energy systems (Fuel Cell, Battery, Ultra-capacitor, Diesel Generator), power conditioning units (MPPT converters, Buck/Boost converters, Battery chargers) and techniques for energy flow management have been discussed in detail. In this paper, an attempt has been made to present a comprehensive review of the research in this area in the past one decade.     

The Penn State intelligent distributed control research laboratory

The authors describe the intelligent distributed controls research laboratory in the College of Engineering at Pennsylvania State University (PSU). The centerpiece of equipment is a modern distributed microprocessor-based control system which was interfaced to real-time simulations of power plant processes. Implementation issues of hierarchical and distributed control for large-scale power plant systems were more fully explored at the university level. The microprocessor-based control system has also been interfaced to the PSU TRIGA nuclear research reactor and enables research in optimal, robust, intelligent, and other advanced control techniques for nuclear power plants     

A systematic review of machine learning methods applied to fuel cells in performance evaluation,durability prediction,and application monitoring

A fuel cell is a power generation device that directly converts chemical energy into electrical energy through chemical reactions; fuel cells are widely used in aerospace, electric vehicle, and small-scale stationary engine applications. The complex phenomena including mass/heat transfer, electrochemical reactions, and ion/electron conduction, can significantly affect the energy efficiency and durability of fuel cells, but are difficult to determine completely. Machine learning (ML) performs well in solving complex problems in engineering applications and scientific research. In this paper, a systematic review is conducted to explore ML methods, including traditional ML and deep learning (DL) methods, applied to fuel cells for performance evaluation (material selection, chemical reaction modeling, and polarization curves), durability prediction (state of health, fault diagnostics, and remaining useful life), and application monitoring. Then comparisons of traditional ML and DL methods are discussed, while the similarities and differences between ML and integrated physics simulations are also concluded. Eventually, the scope of ML methods applied to fuel cells is presented, and outlooks of future researches on ML applications in fuel cells are identified.     

超声波制备生物柴油技术的研究进展

超声波技术作为一门新兴的技术已受到普遍关注.文章综述了近年来国内外超声波技术在生物柴油制备中应用的研究现状、制备原理和优缺点,指出了该技术在生物柴油制备中须要解决的问题,在此基础上提出超声波制备生物柴油技术的发展方向,认为开发更优良的的制备工艺是今后超声波制备生物柴油技术研究领域的主要任务.     

Prediction of selected biodiesel fuel properties using artificial neural network

Biodiesel is an alternative fuel to replace fossil-based diesel fuel. It has fuel properties similar to diesel which are generally determined experimentally. The experimental determination of various properties of biodiesel is costly, time consuming and a tedious process. To solve these problems, artificial neural network (ANN) has been considered as a vital tool for estimating the fuel properties of biodiesel, especially from its fatty acid (FA) composition. In this study, four ANNs have been designed and trained to predict the cetane number (CN), flash point (FP), kinematic viscosity (KV) and density of biodiesel using ANN with logsig and purelin transfer functions in the hidden layer of all the networks. The five most prevalent FAs from 55 feedstocks found in the literature utilized as the input parameters for the model are palmitic, stearic, oleic, linoleic and linolenic acids except for density network with a sixth parameter (temperature). Other FAs that are present in the biodiesels have been considered based on the number of carbon atom chains and the level of saturation. From this study, the prediction accuracy and the average absolute deviation of the networks are CN (96.69%; 1.637%), KV (95.80%; 1.638%), FP (99.07%; 0.997%) and density (99.40%; 0.101%). These values are reasonably better compared to previous studies on empirical correlations and ANN predictions of these fuel properties found in literature. Hence, the present study demonstrates the ability of ANN model to predict fuel properties of biodiesel with high accuracy.     

Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search

This study presents the optimization of biodiesel engine performance that can achieve the goal of fewer emissions, low fuel cost and wide engine operating range. A new biodiesel engine modeling and optimization framework based on extreme learning machine (ELM) is proposed. As an accurate model is required for effective optimization result, kernel-based ELM (K-ELM) is used instead of basic ELM because K-ELM can provide better generalization performance, and the randomness of basic ELM does not occur in K-ELM. By using K-ELM, a biodiesel engine model is first created based on experimental data. Logarithmic transformation of dependent variables is used to alleviate the problems of data scarcity and data exponentiality simultaneously. With the K-ELM engine model, cuckoo search (CS) is then employed to determine the optimal biodiesel ratio. A flexible objective function is designed so that various user-defined constraints can be applied. As an illustrative study, the fuel price in Macau is used to perform the optimization. To verify the modeling and optimization framework, the K-ELM model is compared with a least-squares support vector machine (LS-SVM) model, and the CS optimization result is compared with particle swarm optimization and experimental results. The evaluation result shows that K-ELM can achieve comparable performance to LS-SVM, resulting in a reliable prediction result for optimization. It also shows that the optimization results based on CS is effective.     

A review on modeling of solar photovoltaic systems using artificial neural networks,fuzzy logic,genetic algorithm and hybrid models

The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.     

Design of the measurements validation procedure and the expert system architecture for a cogeneration internal combustion engine

A research activity has been initiated to study the development of a diagnostic methodology, for the optimization of energy efficiency and the maximization of the operational time in those conditions, based on artificial intelligence (AI) techniques such as artificial neural network (ANN) and fuzzy logic.The diagnostic procedure, developed specifically for the cogeneration plant located at the Engineering Department of the University of Perugia, must be characterized by a modular architecture to obtain a flexible architecture applicable to different systems. The first part of the study deals with the identifying the principal modules and the corresponding variables necessary to evaluate the module “health state”.Also the consequent upgrade of the monitoring system is described in this paper. Moreover it describes the structure proposed for the diagnostic procedure, consisting of a procedure for measurement validation and a fuzzy logic-based inference system. The first reveals the presence of abnormal conditions and localizes their source distinguishing between system failure and instrumentation malfunctions. The second provides an evaluation of module health state and the classification of the failures which have possibly occurred. The procedure was implemented in C++.     

Modeling and parameters fitting of chemical and phase equilibria in reactive systems for biodiesel production

Biodiesel, a non-toxic biodegradable fuel from renewable sources such as vegetable oils, has been developed in order to reduce dependence on crude oil and enable sustainable development. The knowledge of phase equilibrium in systems containing compounds for biodiesel production is valuable, especially in the purification stage of the biodiesel. Nonetheless, the refining process of biodiesel and by-products can be difficult and can elevate the production costs considerably unless it has an appropriate knowledge about the phase separation behavior. In addition, the transesterification reaction yield for producing biodiesel depends upon several operation parameters e.g. the feed molar ratio oil-to-alcohol and the temperature. These parameters were analyzed through a thermodynamic analysis by direct Gibbs energy minimization method in this paper, with the purpose of calculating the chemical and phase equilibrium of some mixtures containing compounds found in biodiesel production. For this, optimization techniques associated with the GAMS^® 2.5 software were utilized and the UNIQUAC and NRTL models were applied to represent the non-idealities of the liquid phases. Also, binary interaction parameters of studied compounds were correlated for NRTL and UNIQUAC models by using the least squares principle. The results showed that the use of optimization techniques associated with the GAMS software are useful and efficient tools to calculate the chemical and phase equilibrium by minimizing the Gibbs energy. Moreover, a good agreement was observed in cases in which calculated data were compared with experimental data.     

A dynamic compact thermal model for data center analysis and control using the zonal method and artificial neural networks

Full-scale data center thermal modeling and optimization using computational fluid dynamics (CFD) is generally an extremely time-consuming process. This paper presents the development of a velocity propagation method (VPM) based dynamic compact zonal model to efficiently describe the airflow and temperature patterns in a data center with a contained cold aisle. Results from the zonal model are compared to those from full CFD simulations of the same configuration. A primary objective of developing the compact model is real-time predictive capability for control and optimization of operating conditions for energy utilization. A scheme is proposed that integrates zonal model results for temperature and air flow rates with a proportional–integral–derivative (PID) controller to predict and control rack inlet temperature more precisely. The approach also uses an Artificial Neural Network (ANN) in combination with a Genetic Algorithm (GA) optimization procedure. The results show that the combined approach, built on the VPM based zonal model, can yield an effective real-time design and control tool for energy efficient thermal management in data centers.     

State of the art of geothermal reservoir simulation

Computer modeling of geothermal systems has become a mature technology with application to more than 100 fields world-wide. Large complex three-dimensional models having computational meshes with more than 4000 blocks are now used routinely. Researchers continue to carry out fundamental research on modeling techniques and physical processes in geothermal systems. The new advances are adopted quickly by the geothermal industry and have also found application in related areas such as nuclear waste storage, environmental remediation and studies of the vadose (unsaturated) zone. The current state-of-practice, recent advances and emerging trends in geothermal reservoir simulation are reviewed.     

Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network

This study deals with artificial neural network (ANN) modeling of a diesel engine using waste cooking biodiesel fuel to predict the brake power, torque, specific fuel consumption and exhaust emissions of the engine. To acquire data for training and testing the proposed ANN, a two cylinders, four-stroke diesel engine was fuelled with waste vegetable cooking biodiesel and diesel fuel blends and operated at different engine speeds. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The experimental results revealed that blends of waste vegetable oil methyl ester with diesel fuel provide better engine performance and improved emission characteristics. Using some of the experimental data for training, an ANN model was developed based on standard Back-Propagation algorithm for the engine. Multi layer perception network (MLP) was used for non-linear mapping between the input and output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. It was observed that the ANN model can predict the engine performance and exhaust emissions quite well with correlation coefficient (R) 0.9487, 0.999, 0.929 and 0.999 for the engine torque, SFC, CO and HC emissions, respectively. The prediction MSE (Mean Square Error) error was between the desired outputs as measured values and the simulated values were obtained as 0.0004 by the model.     

Biodiesel combustion: Advances in chemical kinetic modeling

Burgeoning global demand for energy has increased concerns about the fuel security issues and deleterious environmental impacts that result from the ubiquitous use of fossil fuels to meet these needs. This article is a review of completed work towards the goal of creating chemical kinetic mechanisms for biodiesel, which will aid in the development of clean and efficient combustors that utilize alternative fuels. As the composition of biodiesel is too complex to directly model, efforts have instead focused on the development of mechanisms for surrogates, simpler molecules that can produce the primary characteristics of biodiesel combustion. Research initially targeted smaller molecules like methyl butanoate to investigate the role of the characteristic ester group that is present in the fatty acid alkyl esters that comprise biodiesel. The study of isomers and similar unsaturated compounds elucidated the effects of molecular structure on combustion. Subsequent efforts involved the study of larger molecules that are close in scale to biodiesel molecules, such as methyl decanoate, as well as molecules that are present in biodiesel, such as methyl stearate. Applications of kinetic modeling demonstrate its utility in the study of combustion through, for example, revealing the chemistry in the early formation of CO₂ in biodiesel and its soot reduction tendencies. The results of this review illustrate key limitations in kinetic modeling, namely a need for high-pressure kinetic methodology and a need for continuous improvement of kinetic mechanisms through theory and experiment. These limitations suggest direction for future research; further experimental and theoretical work will produce accurate mechanisms for appropriate biodiesel surrogates. All of these efforts represent significant advances in kinetic modeling that are important towards the goal of building a predictive capability for biodiesel combustion. Such predictive capability will aid the development of combustion technologies that will help society meet its energy needs in an environmentally conscious manner.