WSEAS Transactions on Power Systems
Print ISSN: 1790-5060, E-ISSN: 2224-350X
Volume 12, 2017
Economically Realizable Solar Process Heat Solutions in Ethiopian Textile Industry with Demand Derived from Artificial Neural Network Data
Authors: , ,
Abstract: The possibility of augmenting industrial process using solar and thus reach at an economically realizable solution nictitates knowing the energy demand at a relatively high accuracy. An accurate energy use prediction gives relevant information to make sound decisions such as appropriate technology selection, sizing and performance validation of solar process heat integration. Furthermore, an accurate energy prediction would have huge impact in assessing the economic feasibility of the chosen technology. In this paper, textile industry’s thermal energy demand is derived from an artificial neural network (ANN) model. The outputs of this derived thermal model is then coupled to a sun and solar collector steady state model to arrive at an economically realizable solar process heat solutions in a textile industry. To demonstrate the validity and practicality of the proposed solution, a case study was conducted on an Ethiopian textile industry. Payback periods that span from 2.1 to 9.1 years are identified for the various solar collector technologies without subsidy. The payback period is relatively high for parabolic through collector (PTC) that exceeds the typical industry standard of 3-5 years which might create realization barrier. However, as done elsewhere, proper policy to support such renewable solutions would help for the uptake of this technology in Ethiopian industries.
Search Articles
Keywords: Artificial neural network (ANN), solar process heat, sun model, solar collector, economically realizable, payback period, textile industry
Pages: 210-219
WSEAS Transactions on Power Systems, ISSN / E-ISSN: 1790-5060 / 2224-350X, Volume 12, 2017, Art. #24