The consumption of fossil fuels has exponentially increased in recent decades, despite significant air pollution, environmental deterioration challenges, health problems, and limited resources. Biofuel can be used instead of fossil fuel due to environmental benefits and availability to produce various energy sorts like electricity, power, and heating or to sustain transportation fuels. Biodiesel production is an intricate process that requires identifying unknown nonlinear relationships between the system input and output data; therefore, accurate and swift modeling instruments like machine learning (ML) or artificial intelligence (AI) are necessary to design, handle, control, optimize, and monitor the system. Among the biodiesel production modeling methods, machine learning provides better predictions with the highest accuracy, inspired by the brain’s autolearning and self-improving capability to solve the study’s complicated questions; therefore, it is beneficial for modeling (trans) esterification processes, physicochemical properties, and monitoring biodiesel systems in real-time. Machine learning applications in the production phase include quality optimization and estimation, process conditions, and quantity. Emissions composition and temperature estimation and motor performance analysis investigate in the consumption phase. Fatty methyl acid ester stands as the output parameter, and the input parameters include oil and catalyst type, methanol-to-oil ratio, catalyst concentration, reaction time, domain, and frequency. This paper will present a review and discuss various ML technology advantages, disadvantages, and applications in biodiesel production, mainly focused on recently published articles from 2010 to 2021, to make decisions and optimize, model, control, monitor, and forecast biodiesel production.

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机器学习在生物柴油生产研究中的应用综述

尽管存在严重的空气污染、环境恶化挑战、健康问题和有限的资源，但近几十年来化石燃料的消耗量呈指数增长。由于环境效益和可用性，生物燃料可以代替化石燃料用于生产各种能源，如电力、电力和供暖或维持运输燃料。生物柴油生产是一个复杂的过程，需要识别系统输入和输出数据之间未知的非线性关系；因此，必须使用机器学习 (ML) 或人工智能 (AI) 等准确而快速的建模工具来设计、处理、控制、优化和监控系统。在生物柴油生产建模方法中，机器学习以最高的准确度提供更好的预测，受到大脑的自动学习和自我改进能力的启发，以解决研究中的复杂问题；因此，它有利于模拟（反式）酯化过程、物理化学特性和实时监测生物柴油系统。生产阶段的机器学习应用包括质量优化和估计、工艺条件和数量。排放成分和温度估计以及电机性能分析在消耗阶段进行研究。脂肪酸甲酯作为输出参数，输入参数包括油和催化剂类型、甲醇油比、催化剂浓度、反应时间、域和频率。本文将回顾并讨论各种 ML 技术的优点、缺点和在生物柴油生产中的应用，