Comparative Study of Soft Computing and Metaheuristic Models in Developing Reduced Exhaust Emission Characteristics for Diesel Engine Fueled with Various Blends of Biodiesel and Metallic Nanoadditive Mixtures: An ANFIS-GA-HSA Approach

Osama Khan; Mohd Zaheen Khan; Md Toufique Alam; Amaan Ullah; Mohamed Abbas; C Ahamed Saleel; Saboor Shaik; Asif Afzal

doi:10.1021/acsomega.2c05246

Comparative Study of Soft Computing and Metaheuristic Models in Developing Reduced Exhaust Emission Characteristics for Diesel Engine Fueled with Various Blends of Biodiesel and Metallic Nanoadditive Mixtures: An ANFIS-GA-HSA Approach

ACS Omega. 2023 Feb 13;8(8):7344-7367. doi: 10.1021/acsomega.2c05246. eCollection 2023 Feb 28.

Authors

Osama Khan¹, Mohd Zaheen Khan², Md Toufique Alam³, Amaan Ullah³, Mohamed Abbas^{4

5}, C Ahamed Saleel⁶, Saboor Shaik⁷, Asif Afzal^{8

9}

Affiliations

¹ Department of Mechanical Engineering, Faculty of Engineering & Technology, Jamia Millia Islamia, New Delhi110025, India.
² Department of Mechanical Engineering, Institute of Engineering & Technology, Lucknow226021, India.
³ Department of Mechanical Engineering, Al-Falah University, Faridabad121004, Haryana, India.
⁴ Electrical Engineering Department, College of Engineering, King Khalid University, Abha61421, Saudi Arabia.
⁵ Electronics and Communications Department, College of Engineering, Delta University for Science and Technology, Gamasa35712, Egypt.
⁶ Department of Mechanical Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha61421, Saudi Arabia.
⁷ School of Mechanical Engineering, Vellore Institute of Technology, Vellore632014, Tamil Nadu, India.
⁸ Department of Mechanical Engineering, P. A. College of Engineering (Affiliated to Visvesvaraya Technological University, Belagavi), Mangaluru574153, India.
⁹ University Centre for Research & Development, Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali140413, Punjab, India.

Abstract

Since the discovery of petrol-based products, a surge in energy-requiring equipment has been established across the world. Recent depletion of the existing crude oil resources has motivated researchers to opt for and analyze potential fuels that could potentially provide a cost-effective and sustainable solution. The current study selects a waste plant known as Eichhornia crassipes through which biodiesel is generated, and its blends are tested in diesel engines for feasibility. Different models using soft computing and metaheuristic techniques are employed for the accurate prediction of performance and exhaust characteristics. The blends are further mixed with nanoadditives, thereby exploring and comparing the changes in performance characteristics. The input attributes considered in the study comprise engine load, blend percentage, nanoparticle concentration, and injection pressure, while the outcomes are brake thermal efficiency, brake specific energy consumption, carbon monoxide, unburnt hydrocarbon, and oxides of nitrogen. Models were further ranked and chosen based on their set of attributes using the ranking technique. The ranking criteria for models were based on cost, accuracy, and skill requirement. The ANFIS harmony search algorithm (HSA) reported a lower error rate, while the ANFIS model reported the lowest cost. The optimal combination achieved was 20.80 kW, 2.48047, 150.501 ppm, 4.05025 ppm, and 0.018326% for brake thermal efficiency (BTE), brake specific energy consumption (BSEC), oxides of nitrogen (NOx), unburnt hydrocarbons (UBHC), and carbon monoxide (CO), respectively, thereby furnishing better results than the adaptive neuro-fuzzy interface system (ANFIS) and the ANFIS-genetic algorithm model. Henceforth, integrating the results of ANFIS with an optimization technique with the harmony search algorithm (HSA) yields accurate results but at a comparatively higher cost.