A systematic and critical review on effective utilization of artificial intelligence for bio-diesel production techniques. (15th April 2023)

• Record Type: Journal Article
• Authors: Ahmad, Junaid; Awais, Muhammad; Rashid, Umer; Ngamcharussrivichai, Chawalit; Raza Naqvi, Salman; Ali, Imtiaz
• Is Part Of: Fuel. Volume 338(2023)
• Publication Date: 2023-04-15
• Subjects: AI Artificial Intelligence -- AMT Adaptive Motion Trainer -- ANFIS Adaptive Neuro Fuzzy Inference System -- ANN Artificial Neural Networks -- ASTM American Standard Testing Methods -- AV Acid Value -- BRNN Bidirectional Recurrent Neural Networks -- CN Cetane Number -- CNN Convolutional Neural Networks -- ELM Elaboration Likelihood Model -- EN European Nation -- ESIM Evolutionary Support Vector Machine Inference Model -- FAME Fatty Acid Methyl Ester -- FFA Free Fatty Acid -- GA Genetic Algorithm -- GC Gas Chromatography -- GMM Gaussian Mixture Model -- GPR Gaussian Process Regression -- KCGA K-means Chaotic Genetic Algorithm -- KELM Kernel Extreme Learning Machines -- KNN K-Nearest Neighbors -- LSSVM Least Squares Support Vector Machine -- LSTM Long/Short Term Memory -- ML Machine Learning -- MLPNN Multiple Layer Perceptron Neural Network -- RBF Radial Basis Function -- RBFNN Radial Basis Function Neural Network -- RSM Response Surface Methodology -- SA Sentiment Analysis -- SVM Support Vector Machines; Systematic review -- Biodiesel -- Artificial Intelligence -- Process optimization -- Prediction and validation of models; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2022.127379
• Languages: English
• ISSNs: 0016-2361

Abstract

Graphical abstract: Highlights: Biodiesel production and system optimization. Role of artificial intelligence in the biodiesel production. Artificial intelligence-based models for the prediction of fuel properties. Abstract: Since industrial development and globalization of the world, fossil fuels remain a major source of energy for almost all sectors of life. Fossil fuels, without doubt, play a vital role in the development of today's world. However, there are increasing reservations about the consumption of fossil fuels due to their detrimental impact on our ecosystem. In recent decades, biodiesel has attracted much attention as a promising replacement for fossil fuel-based diesel, especially in the transportation sector. Biodiesel is a non-toxic, environmentally friendly, and carbon-free fuel that can be made from any kind of vegetable oil or fat. In recent developments, numerous techniques have been developed to efficiently prepare biodiesel from oil/fats. Therefore, in this paper, we cover a new advancement in techniques used for the conversion of oil/fat to biodiesel and the role of artificial intelligence (AI). AI approaches help predict the effectiveness of biodiesel production techniques and optimize the process, in addition to minimizing the cost of the process. The AI-enabled biodiesel prediction methods consist of several stages, i.e., biodiesel data collection, biodiesel data preprocessing, developing, and tuning machine learning (ML) algorithm on biodiesel data, and predicting unknown biodiesel properties. Therefore, the main purpose of applying AI to the biodiesel production process is to improve the process optimization and to develop AI models for fuel properties measurements and ultimately reduce the cost of the product. Because these problems have not been addressed previously, we hope that our analysis will help future researchers identify the appropriate technique and feedstock to produce high-quality biodiesel.