Computers & Chemical Engineering ( IF 4.3 ) Pub Date : 2020-08-04 , DOI: 10.1016/j.compchemeng.2020.107048 Muhammad Shahbaz , Syed Ali Ammar Taqvi , Muddasser Inayat , Abrar Inayat , Shaharin A. Sulaiman , Gordon McKay , Tareq Al-Ansari
The air gasification of Palm Kernel Shells (PKS) using coal bottom ash (CBA) as a catalyst has been performed in a fixed-bed gasifier. The impact of three process parameters, namely, temperature (575–775°C), air flowrate (1.5–45 litter/min) and catalyst loading (0–30 wt.%) has been investigated on the product gas yield. The composition of the H2 product is computed to be a maximum of 28 vol.% at 875°C. The air flowrate has a direct relation with H2 production. The catalysts used have demonstrated a positive impact on the carbon conversion efficiency, showing the increase in carbon-containing gases in the product gas due to the increases in gas yield. A Non-linear Autoregressive Network with exogenous inputs (NARX) neural network has been used to predict the gaseous flowrate dynamically in order to improve gasification performance. The predicted results from the NARX network demonstrate good agreement with the experimental study with R2 ≥ 0.99.
中文翻译:
固定床下浮式气化炉中的空气催化生物质(PKS)气化,利用废底灰作为催化剂,采用NARX神经网络建模
使用煤灰(CBA)作为催化剂的棕榈仁壳(PKS)的空气气化已在固定床气化炉中进行。三个工艺参数的影响,即温度(575–775°C),空气流速(1.5–45升/分钟)和催化剂负载量(0–30 wt。%)已针对产物气产率进行了研究。计算出H 2产品的成分在875时最大为28 vol。%℃。空气流量与H 2的产生有直接关系。所使用的催化剂已显示出对碳转化效率的积极影响,表明由于气体产率的增加,产物气体中含碳气体的增加。具有外源输入的非线性自回归网络(NARX)神经网络已用于动态预测气态流量,以提高气化性能。从NARX网络预测的结果证明与实验研究具有良好的协议- [R 2 ≥0.99。