Prediction of Emissions and Profits from a Biomass, Tyre, and Coal Fired Co-Gasification CHP Plant Using Artificial Neural Network: Nigerian and South African Perspectives

Abstract

The local sourcing of feedstock for energy generation will reduce costs in the power plant, and promote energy sustainability. Most times, potential investors in this area show interest about understanding the profitability of the business because, the information boosts the confidence of the investors in the project, and gives them the opportunity of making a short and long term plans about the business. The emissions arising from the energy plant is an important aspect of the venture that requires proper attention, otherwise the costs of emission control may consume a greater part of the profit, hence rendering the business un-viable. Nigeria and South Africa (SA) have abundant biomass (e.g. corn cob, sugarcane bagasse, & pine saw dust) coal and tyre that can be used as fuel in an energy plant. A 10 MW CHP plant was fired with coal and biomass, and tyre obtained from Nigeria and South Africa (SA) respectively, at ratios of 1:1, 3:2, and 4:1 to study the emissions and profits in the plant. An empirical model was employed to estimate the annual amount of feedstock and feed rate required for the plant, after which, an artificial neural network (ANN); Levenberg-Marquardt algorithm was used to predict the emissions and profits in the plant for 20-year-investment period with feedstock costing (WFC) and without feedstock costing (WOFC). The profit obtained from the South African feedstock, WFC and WOFC; produced about 45.18 % and 36.83 % ($3, 900, 000.07 and $3, 179, 184.49) higher profits than the Nigerian feedstock, but the CO, NOX, & SO2 emissions from Nigerian feedstock were lower than that of SA. The findings from this study could be used as a platform for decision making by potential investors and stake-holders, and further research and development in the area.

Evaluation of technological tendencies in the carbon capture process: a review.

“…In general, two requirements should be considered when selecting the sorbent: (i) Reactivity of the gas and liquid, (ii) Solubility of the gas in the liquid. ACC technology has advantages such as [41] process reliability, high absorption efficiency, modularity when integrated into processes compared to other technologies [42], system without human operation [43], and purity levels exceeding 95% [41]. However, it still has limitations to be applied in power plants, as well as low process performance, high energy demand, and high investment, operation and maintenance costs [33], limiting its application at the commercial level [41].…”