Modelling and Optimisation of High Pressure Water Scrubbing of Biogas for CO2 removal using Response Surface Methodology and Artificial Neural Networks

Muhammad I. J., Amenaghawon N. A., Ajieh M. U.

Abstract


Biogas produced from anaerobic digestion of organic wastes consists of mainly methane (CH4) and undesirable gases like carbon dioxide (CO2), hydrogen sulphide (H2S) and other trace gases. This study focused on the modelling and optimisation of biogas upgrading using high pressure water scrubbing (HPWS). The biogas upgrading process was simulated using Aspen HYSYS while the experiments were designed using Design Expert software. Response surface methodology (RSM) and artificial neural network (ANN) was adopted to model and optimise the process. The four factors considered were absorber pressure, biogas flowrate, and water flow rate and absorber temperature. RSM analysis yielded a quadratic model for predicting the chosen responses (CH4 recovered and CO2 captured) as a function of the four independent variables. For ANN, a multilayer full feed forward neural network trained with the Levenberg-Marquardt algorithm was found suitable for modelling the upgrading process. Both the RSM and ANN models described the upgrading process (methane recovery) with high accuracy as indicated by their high R2 (0.9826 and 0.9999), low RMSE (1.3937 and 0.1200) and low AAD (0.0172 and 0.0006) values respectively. Optimisation results showed that up to 93% CH4 recovery and 87% CO2 capture was achieved using HPWS and this was obtained at a condition of absorber pressure (18 bar), biogas flowrate (7 m3/h), water flow rate (148 m3/h) and absorber temperature (11 oC). When compared, the ANN model performed better in predicting the responses than RSM.


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