Bioenergy supply chain optimisation: the case study of South Africa

This study involves desktop research on the development of a mixed integer linear programming (MILP) model to optimize the multiple timeframe supply chain network production of bioenergy products based on economic, environmental and sustainability objectives. The study also assesses the effects of varied dry matter and methane contents on electricity production from biogas whereby the General Algebraic Modelling System (GAMS) optimisation software with CPLEX solver is utilised. To develop the model, a four-layer supply chain approach is applied which includes raw materials harvesting and acquisition, primary conversion and secondary conversion technologies and distribution to demand zones. It includes additional features such as storages of raw materials, intermediate and final products, recycle and reuse of intermediate and final products, different transportation modes, variations in production (monthly- or hourly-based), model reduction techniques and single- and multi-objective optimisation, among other features. Most of the data used are from the literature while a limited portion is retrieved from semi-formal correspondences with experts. The model has been firstly applied to electricity production from biogas. The results obtained show that for a case study of three locations in the Western Cape province of South Africa, a profit-after-tax of about 3.1 * 106 $/y is obtained with a dominant feedstock selection of poultry manure, wheat silage and triticale silage when biogas is used to generate 999 kW electricity. Subsequently, a profit-after-tax of approximately 10 * 106 $ /y is accrued when the model is extended to incorporate biodiesel production from waste cooking oil and bioethanol generation from corn stover and forestry residues. It is interesting to note that a tri-criteria objective optimisation of the supply chain network generation of electricity from biogas is also considered using the modified goal method approach. The objectives are to maximize profit-after-tax, environmental unburdening, and sustainability profit. The results indicate a maximum profit-after-tax of about 3.1 * 106 $/y like that obtained in the single objective optimisation. Furthermore, the maximum environmental unburdening and maximum sustainability profit are 4.54 * 103 t CO2 eq./y and 1.135 * 106 $/y. The optimally selected feedstocks in all three objectives scenarios are wheat silage and triticale silage. The results show promise which could be used as a decision support tool for policymakers. Hence, the novelty of this study is a model which integrates dry matter and methane content of feedstocks; the spherical law of cosines equation to calculate the distance between coordinate points; maximised trade-offs of economic, environmental and sustainability profits.

Grantor

Department of Chemical Engineering

Year dc:date.issued

2019

Author dc:creator

• Egieya, Jafaru Musa

Advisors dc:contributor.advisor

• Isafiade, Adeniyi J
• Lidija, Čuček

Handle dc:identifier.uri

http://hdl.handle.net/11427/30451

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/30451