Elsevier

Energy

Volume 140, Part 1, 1 December 2017, Pages 566-583
Energy

A decision support system to evaluate the optimum fuel blend in an IC engine to enhance the energy efficiency and energy management

https://doi.org/10.1016/j.energy.2017.08.051Get rights and content

Highlights

  • A new insight of MCDM application to evaluate the best fuel blend

  • Analysis of engine performance of fish oil biodiesel.

  • Hybrid MCDM techniques of Fuzzy TOPSIS and Fuzzy VIKOR to rank the blends.

Abstract

The demand for the energy has increased drastically as a result of the rapid growth in industrialization, urbanisation and higher standard of living. One such potential substitute to fossil fuels is biodiesel that ensures sustainable energy source. The selection of appropriate source of biodiesel and proper blending of biodiesel plays a major role in alternate energy production. In the present work, a novel hybrid Multi Criteria Decision Making (MCDM) technique was proposed to evaluate and select the optimum fuel biodiesel blend for the IC engine with conflicting criteria to enhance the energy efficiency. Exploratory analysis were carried out on a single cylinder four stroke, air cooled, constant speed, direct injection diesel engine with a rated output of 4.4 kW at 1500 rpm at different loads. Two hybrid MCDM models, namely Fuzzy TOPSIS and Fuzzy VIKOR were proposed. Fuzzy was applied to determine the relative weights of the evaluation criteria whereas TOPSIS and VIKOR were applied to obtain the final ranking of alternatives. Diesel, B20, B40, B60, B80 and B100 fuel blend alternatives are prepared by varying the proportion of biodiesel for MCDM model. Similarly BTE, MRPR, NOx, CO2, CO, HC, SMOKE, ID, CD and Exhaust gas temperature were considered as the evaluation criteria. The ranking order by Fuzzy TOPSIS is based on closeness coefficient and Fuzzy VIKOR is based on VIKOR index. In Fuzzy TOPSIS, B40 stands first at 50% and 75% load conditions and second at 25% and full load conditions respectively. In Fuzzy VIKOR, B40 stands first at 25% and 50% conditions and second at no load, 75% and full load conditions respectively. The ranking of alternatives as obtained by both Fuzzy-TOPSIS and Fuzzy-VIKOR is B40 > B20 > Diesel > B80 > B60 > B100 and B40 > B20 > Diesel > B60 > B80 > B100. From the results, it was observed that both the methods indicated that B40 is the best blend to operate the engine. Hence, it is concluded that mixing 40% biodiesel with diesel is suggested as a good partial replacement for diesel. This paper highlights a new insight into MCDM techniques to evaluate the best fuel blend for the decision makers such as engine manufactures and R& D engineers to meet the fuel economy and emission norms to empower the green revolution and energy management.

Introduction

Energy is the primary parameter which directly impacts the country's economy as stated by World Trade Organization. According to International Energy Information Administration, a projection has been made that the oil resources would be sufficient enough to meet the demand up to 2030. (Energy outlook, 2030). Astonishing advances in industrialization and transportation, building, and innumerable other technologies in the world has led to a steep rise for the demand of petroleum-based fuels. But, the energy consumption continues to increase with the growth of world population leads to energy depletion and environmental degradation. In order to address both these problems of energy requirement and energy depletion, the search for a viable alternative fuel was carried out. Biomass fuels are the convincing alternative for the fossil fuels which guarantees energy security but also provides renewable, biodegradable and 100% natural fuel with properties similar to diesel. It also reduces global warming and environmental meltdown occurring due to massive carbon footprints left by the fossil fuels. Considerable research work is going around the globe in search of alternative renewable fuels for diesel engines. The important advantages in using biodiesel as an alternate fuel is that it can be used as a fuel in a diesel engine without any modification.

Biodiesel can be produced from renewable resources such as vegetable oil, animal fat and waste cooking oil [1]. The cultivation of crops for biodiesel production poses a threat to food security and contributes to decline in soil fertility [2]. On the other hand, the animal fat present in the waste parts of fish serves to be a good source of crude oil for biodiesel. Jayasinghe and Hawboldt (2012) reviewed the properties of biofuel from fish and suggested that it exhibits better engine performance [3]. A few researchers have used fish oil as a biodiesel to study the performance and emission characteristics of Internal Combustion (IC) engine [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15]. Hence an attempt has been made to use ethyl ester of fish oil as a biodiesel to study the performance, exhaust emissions and combustion parameters. Sustainability of biodiesel is also the key factor for using it as a fuel in diesel engines Evaluation of suitable blend is based on the performance, emission and combustion parameters of the engine. It is quite difficult to choose the optimum blend to run the Internal Combustion (IC) engine with respect to different load conditions for different biodiesel-diesel blends. In the existing work, most of the researchers have discussed their operating fuel with reference of NOx, smoke and BTE performance. Based on the reduction of NOx and smoke and increase of BTE, the operating fuel is recommended as the best blend without considering other influencing parameters such as HC and CO [16], [17], [18]. In this paper, to overcome the shortcomings of the existing research, all the performance, emission and combustion characteristics are considered.

The conventional testing of engine for fuel blends under all possible conditions is more complex, time consuming and expensive at different load conditions with different fuel blends. Modelling of the engine operation using optimization technique is the alternate method to meet out the aforementioned requirements. There are a few studies in literature with regards to the investigation of optimum blends ratios of biodiesel blends using artificial neural network (ANN), response surface methodology (RSM). Engine performance characteristics are optimised using ANN which has been gradually increasing over the period of a few years [42], [43], [44], [45], [46]. Taguchi method, Response surface methodology and Factorial design methods are also proposed by the researchers to improve the engine performance characteristics and exhaust emissions and to identify the best possible blend ratios in a diesel engine without any engine modification [47], [48], [49], [50]. In the existing work, most of the researchers have discussed their ANN, RSM and Taguchi technique to optimize the engine performance but there is no attempt made by the researchers to choose the optimum blend to run the IC engine using MCDM.

MCDM provides sophisticated methodological tools that are oriented towards the support of the decision makers in facing complex real-world decisions. The application of MCDM in IC engine has been gradually increasing in the past few decades. Poh and Ang (1999) applied an AHP technique to identify and evaluate the best alternative fuel for land transportation in Singapore [19]. Janic and Reggiani (2002) applied SAW (Simple Additive Weighting), TOPSIS(Technique for Order Preference Similarity to Ideal Solution) and AHP to evaluate the selection of new hub airport [20]. Winebrake and Creswick (2003) have predicted the future of hydrogen fuelling systems for transportation using multi-criteria decision making method with Analytical Hierarchy Process [21]. Tzeng et al. (2005) applied multi-criteria technique TOPSIS and VIKOR to evaluate the alternative fuel buses for public transportation [22]. Sakthivel et al. (2013) applied MCDM for evaluating an automobile purchase model. Kim, Lee & Cho (2011) proposed Fuzzy TOPSIS method to evaluate the product diffusion for an automobile [23]. A Yidliz (2016) proposed Fuzzy TOPSIS to evaluate the automobile selection problem [24]. Chandrasekhar and Raja (2016) proposed Fuzzy TOPSIS to evaluate the material selection for Automobile Torsion bar [25]. Mayyas, Omar and Hayajneh (2016) proposed an eco-material selection approach specific to the automobile body panels using a Fuzzy TOPSIS [26]. Girubha and Vinodh (2016) proposed the environmental impact analysis for the material selection of an automotive component using Fuzzy VIKOR approach [27].

From the literature, there is no trace of research that deals with selection of suitable fuel blend based on the performance, combustion and emission characteristics using MCDM technique Fuzzy TOPSIS and Fuzzy VIKOR. This paper proposed a novel MCDM technique for evaluating optimum blend. In this paper, two MCDM methods Fuzzy TOPSIS and Fuzzy VIKOR are applied to identify the suitable blend to achieve maximum engine performance and environmental benefits by reducing noxious emissions. List of all alternatives and different performance criteria are taken into account for these methods. The performance ranking of all alternatives are computed using these two methods are also compared with each other method which prove the best of these methods.

Section snippets

Fuel properties

The fish oil ethyl ester contained no suspended matter and had an undesirable smell peculiar to fish oil. Its colour was transparent and light yellow. There were a number of fuel properties that had to be investigated before using fish oil as biodiesel, to ensure proper operation of diesel engine. Several blends of varying concentrations such as B0 (Diesel 100%), B20 (Ethyl ester of fish oil 20%: Diesel 80%), B40 (Ethyl ester of fish oil 40%: Diesel 60%), B60 (Ethyl ester of fish oil 60%:

Experimental procedure

The engine tests were carried out with a single cylinder, four stroke, air cooled and compression ignition engine of constant speed due to the benefits like availability and robustness. The schematic diagram of the engine setup is shown in Fig. 1. The engine was coupled to an electrical dynamometer with a control system to provide the brake load. The fuel consumption was measured by a burette and a stop watch. A provision was made to mount a piezoelectric pressure transducer on the cylinder

The proposed methodology

The proposed methodology consists of three basic stages (1) Identification of the performance and emission (2) Form the decision committee and tabulate according to the aggregated weights (3) Ranking the alternatives using Fuzzy TOPSIS to select the apt blend. The schematic flow chart of the proposed methodology for the selection of the best blend is shown in Fig. 1. In the first stage, the alternative blends and their evaluation criteria are identified, and then a decision hierarchy is framed.

Results and discussion

The decision hierarchy diagram is created with the identified evaluation criteria and the alternative blends as in Fig. 2. This hierarchy mainly consists of three levels, namely, the main objective of the problem, criteria and the alternatives, which are positioned at the high level, second level and the bottom level respectively. After the construction of the hierarchy diagram, the fuzzy membership function sets are generated with the pair-wise comparison of the criteria in order to determine

Conclusion

The fuel blends are one of the important aspects in the present world as the present research is totally focused on using biodiesel blends as a replacement for the diesel mixture. To ascertain best blend, number of performance, emission and combustion parameters are to be considered, which involves a multidimensional perspective with major focus on meeting the stringent emission norms to improve the energy efficiency. Therefore, effective decision-making approach is essential to resolve the

Dr. G. Sakthivel is an Associate professor, Mechatronics division, School of Mechanical and Building Sciences, VIT University Chennai, India. He completed his doctorate in the area of optimization in engine modelling and design and alternative fuels in the year 2013. He has published twenty three papers in international journals and more than fifteen papers in international and national Conferences He is a life member of ISTE. His research interest is optimization in IC engines, and automotive

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    Dr. G. Sakthivel is an Associate professor, Mechatronics division, School of Mechanical and Building Sciences, VIT University Chennai, India. He completed his doctorate in the area of optimization in engine modelling and design and alternative fuels in the year 2013. He has published twenty three papers in international journals and more than fifteen papers in international and national Conferences He is a life member of ISTE. His research interest is optimization in IC engines, and automotive electronics.

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    Dr. Bernard. W.Ikua, Professor, School of Mechanical, Manufacturing and Materials Engineering, College of Engineering and Technology JKUAT, Nairobi. He has published several publications more than 35 papers in International journals and 37 papers in international conferences. His research interest is Modelling and control of machining processes and intelligent optimization.

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