The looming global crisis over increasing greenhouse gases and rapid depletion of fossil fuels are the motivation factors for researchers to search for alternative fuels. There is a need for more sustainable and less polluting fuels for internal combustion engines. Biomass offers significant potential as a feed material for gasification to produce gaseous fuel. It is carbon neutral, versatile, and abundant on earth. The present study thus explores a mix of different feedstocks, such as mahua wood and low-grade coal for downdraft gasifiers. The resultant producer gas (PG), after cooling-cleaning will be used as the gaseous fuel to run the diesel engine in dual-fuel mode, while a tiny quantity of linseed biodiesel-diesel blends as B20 (20 % biodiesel + 80 % diesel) will be supplied as injected pilot fuel. The data from experimental work at different engine operation settings was employed to develop a prediction-optimization model using a twin approach of RSM and Grey wolf optimization (GWO). The three control factors for the engine were compression ratio (CR) 17 – 17.5 – 18, equivalence ratio 0.12–0.41, and engine loads in the range of 10–100 % were used to collect data on response variables i.e., brake-thermal efficiency (BTE) and emission data (CO2, NOx, UHC, and CO). A comparative approach of RSM and GWO was utilized for the multi-objective optimization revealing the best results were attained at 17.65 CR, 0.4 ER, 82.55 % engine load in the case of GWO.

中文翻译：

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使用下吸式气化炉结合双燃料发动机系统对废弃生物质-低品位煤混合物进行共气化：使用 RSM 和 Grey Wolf Optimizer 的混合方法进行多目标优化


温室气体增加和化石燃料迅速枯竭带来的迫在眉睫的全球危机是研究人员寻找替代燃料的动机因素。内燃机需要更可持续、污染更少的燃料。生物质作为气化生产气体燃料的原料具有巨大的潜力。它是碳中和的，用途广泛，并且在地球上丰富。因此，本研究探讨了不同原料的混合物，例如用于下吸式气化炉的马华木和低品位煤。冷却清洁后产生的产生气体 （PG） 将用作气体燃料，以双燃料模式运行柴油发动机，而少量亚麻籽生物柴油-柴油混合物 B20（20% 生物柴油 + 80% 柴油）将作为喷射引燃燃料供应。来自不同发动机运行设置的实验工作数据用于使用 RSM 和 Grey wolf 优化 （GWO） 的孪生方法开发预测优化模型。发动机的三个控制因子是压缩比 （CR） 17 – 17.5 – 18，等效比 0.12–0.41，发动机负载在 10-100% 范围内，用于收集响应变量的数据，即制动热效率 （BTE） 和排放数据（CO2、NOx、UHC 和 CO）。采用 RSM 和 GWO 的比较方法进行多目标优化，结果显示，在 GWO 的情况下，在 17.65 CR、0.4 ER、82.55% 发动机负载下获得了最佳结果。