Table1_Prediction of Process Parameters for the Integrated Biomass Gasification Power Plant Using Artificial Neural Network.xlsx

Alternative renewable fuels like biomass have the potential to be considered for electricity generation by replacing the utilization of fossil fuels and reducing the greenhouse gas emissions into the environment. An integrated biomass gasification power plant is the best suitable option to generate electricity from different biomass feedstocks. Several modeling and simulation techniques have been utilized for the integrated biomass gasification power generation process. These models are utilized to predict the power output from the different gasifier types, designs, and feedstocks. In this study, An Artificial neural network (ANN) model is developed to estimate the process parameters of the Integrated biomass gasification power plant. This ANN model predicts the gasification temperature (T) and air to fuel ratio (AFR) for the gasification process integrated with the power plant at the atmospheric pressure. There is a total of ten input parameters such as moisture content of biomass (M), volatile matter (VM), fixed carbon (FC), ash content (A), element composition of carbon (C), oxygen (O), hydrogen (H), nitrogen (N), sulfur (S) and required power (KW) are used to predict the two key gasification process parameters T and AFR. The data generated from thermodynamic equilibrium model simulations are employed in the developed ANN model for the different 86 biomass feedstocks. The proposed ANN model was optimized for the Mean Squared Error (MSE) loss function and evaluated using MSE and R score metrics. It is observed that the best predicted for a hidden layer size was of 60 neurons. The best test score was achieved as an MSE score of 1,497 and test R 0.9976. This study can be implemented for any kind of biomass feedstock for the power generation system.

诸如生物质（biomass）在内的替代可再生燃料，可通过替代化石燃料的使用并降低温室气体排放，具备用于发电的潜力。集成式生物质气化发电厂是利用各类生物质原料发电的最优方案。学界已针对集成式生物质气化发电过程开发了多种建模与仿真技术，此类模型可用于预测不同气化炉类型、设计方案及原料对应的发电出力。

本研究开发了一种人工神经网络（Artificial Neural Network, ANN）模型，用于估算集成式生物质气化发电厂的工艺参数。该模型可预测常压下集成发电系统的气化工艺参数：气化温度（T）与空燃比（Air to Fuel Ratio, AFR）。本次研究共采用10项输入参数，包括生物质含水率（Moisture Content, M）、挥发分（Volatile Matter, VM）、固定碳（Fixed Carbon, FC）、灰分（Ash Content, A）、碳元素（Carbon, C）、氧元素（Oxygen, O）、氢元素（Hydrogen, H）、氮元素（Nitrogen, N）、硫元素（Sulfur, S）以及目标发电功率（Kilowatt, KW），用于预测气化过程的两项关键参数T与AFR。

本研究采用热力学平衡模型仿真生成的数据集，涵盖86种不同生物质原料的仿真数据，将其用于所开发的人工神经网络模型训练。所提出的人工神经网络模型以均方误差（Mean Squared Error, MSE）作为损失函数进行优化，并通过MSE与R评分（R score）两项指标进行性能评估。研究发现，当隐藏层神经元数量为60时，模型预测效果最优。模型的最优测试结果为：MSE评分1,497，R评分0.9976。本研究方法可推广应用于各类生物质原料的发电系统。