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In this research, three suitable alkanolamine solutions are selected for sweetening a natural gas with high H2S content and low CO2/H2S ratio. For each process, six different CO2 fractions in the regenerated solution are selected. On the basis of each CO2 fraction in the regenerated solution, the three processes are designed and simulated using the Aspen HYSYS process simulator to rich pipeline specifications (i.e., H2S content lower than 4 ppm and CO2 content lower than 2 mol %) for the sweet gas. The results of simulation are then economically evaluated using Aspen Economic Evaluation software. The results of simulation and economic evaluation indicate that the diglycolamine (DGA) process is more economical compared to monoethanolamine (MEA) and mixed methyldiethanolamine (MDEA) + MEA processes. Also, it is concluded that there are several advantages in operating the alkanolamine sweetening processes at higher CO2 fractions of the regenerated amine. On the basis of the results of this study, lower total capital costs, lower annual operating costs, and lower energy requirements for regeneration of the solution are obtainable by operating the alkanolamine sweetening processes at higher fractions of CO2 in the regenerated solution.

The aim of this study was to develop a model for predicting the performance of a desulfurizing bio-filter (BF), without requiring prior information about H 2 S biodegradation kinetics and mechanism. A single hidden layer artificial neural network (ANN) model was developed and validated using the gradient descent backpropagation (GDBP) learning algorithm coupled with a learning rate and a momentum factor. The ANN model inputs were gas flow rate, residence time, and axial position in the BF bed. The removal efficiency of H 2 S was the model output. Various structures for ANN model, differing in the number of hidden layer neurons, were trained and an early stopping validation technique, the K-fold cross-validation, was used to determine the optimal structure with the best generalization ability. The modeling results showed that there was a good agreement between the experimental data and the predicted values, with a determination coefficient (R 2) of 94%. This implies that the ANN model might be an attractive and useful alternative tool for forecasting the performance of desulfurizing BFs.

Chaiprapat (2019) Single-/triple-stage biotrickling filter treating a H 2 S-rich biogas stream: Statistical analysis of the effect of empty bed retention time and liquid recirculation velocity,

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