The emission of sulphur dioxide (SO2) gas from power plants and factories to the atmosphere has been an environmental challenge globally. Thus, there is a great interest to control the SO2 gas emission economically and effectively. This study aims to use and convert abundantly available oil palm fiber (OPF) biomass into an adsorbent to adsorb SO2 gas. The preparation of OPF biochar and activated biochar was optimised using the Response Surface Methodology (RSM) based on selected parameters (i.e., pyrolysis temperature, heating rate, holding time, activation temperature, activation time and CO2 flowrate). The best adsorbent was found to be the OPF activated biochar (OPFAB) compared to OPF biochar. OPFAB prepared at 753 °C for 73 min of activation time with 497 ml/min of CO2 flow yields the best adsorption capacity (33.09 mg/g) of SO2. Meanwhile, OPF pyrolysed at 450 °C of heating temperature, 12 °C/min of heating rate and 98 min of holding time yield adsorption capacity at 18.62 mg/g. Various characterisations were performed to investigate the properties and mechanism of the SO2 adsorption process. Thermal regeneration shows the possibilities for the spent adsorbent to be recycled. The findings imply OPFAB as a promising adsorbent for SO2 adsorption.
The thermochemical processes such as gasification and co-gasification of biomass and coal are promising route for producing hydrogen-rich syngas. However, the process is characterized with complex reactions that pose a tremendous challenge in terms of controlling the process variables. This challenge can be overcome using appropriate machine learning algorithm to model the nonlinear complex relationship between the predictors and the targeted response. Hence, this study aimed to employ various machine learning algorithms such as regression models, support vector machine regression (SVM), gaussian processing regression (GPR), and artificial neural networks (ANN) for modeling hydrogen-rich syngas production by gasification and co-gasification of biomass and coal. A total of 12 machine learning algorithms which comprises the regression models, SVM, GPR, and ANN were configured, trained using 124 datasets. The performances of the algorithms were evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). In all cases, the ANN algorithms offer superior performances and displayed robust predictions of the hydrogen-rich syngas from the co-gasification processes. The R2 of both the Levenberg-Marquardt- and Bayesian Regularization-trained ANN obtained from the prediction of the hydrogen-rich syngas was found to be within 0.857-0.998 with low prediction errors. The sensitivity analysis to determine the effect of the process parameters on the model output revealed that all the parameters showed a varying level of influence. In most of the processes, the gasification temperature was found to have the most significant influence on the model output.