SOURCE:

Faculty: Engineering
Department: Industrial And Production Engineering

CONTRIBUTORS:

Chukwumuanya, E. O.
Ihueze, C. C.

ABSTRACT:

The study focused on developing models for sustainable municipal solid waste management. Solid waste management in Anambra State, Nigeria, with particular reference to the state capital, Awka was used as a case study. Both qualitative and quantitative data were collected from primary and secondary sources and analyzed. Qualitative data analysis methodology applied include inventory management techniques, strength-weakness-opportunity-threat (SWOT) analysis, causal loop analysis, Fishbone diagram, and Pareto principle. Quantitative data analysis methodology applied include descriptive statistics, optimization techniques, Queuing theory, multiple regression analysis, transition matrix and Markov chain. Forty mathematical and five iconic models were developed in the study. The developed models were simulated using a thirty six month field study data collected on Awka Municipal solid waste Management. Application of inventory management techniques enabled the development of solid waste inventory management (SWIM) theory as a new way of managing solid wastes. Meanwhile, results from the SWOT analysis revealed that Anambra State Waste Management Agency (ASWAMA) has the basic requirements for continuously improving its services to the state. Causal loops were employed in explaining the current system of solid waste management in Anambra State. Moreso, Fishikawa diagram was used in explaining the factors which were constraints to the provision of effective and efficient solid waste management in Anambra State; while the Pareto 80:20 rule was used in investigating the few dominant factors. Four factors namely, insufficient funding, non-availability of vital tools/equipment, lack of modern technology and lack of data/improper data management were the major constraints in Anambra State solid waste management system. Furthermore, optimization results showed that the mean optimum evacuation quantity of solid waste which minimized the objective cost function was 1364 chain-up bin loads and the optimum total cost as NGN 9933904 with R2 > 0.84. The solid waste per capita disposal rate in Awka area was evaluated as 0.189 kg/capita/day, and the Markov model prediction result gave a long time percentage monthly solid waste contributions from each of the twelve zones of Awka city to the total waste stream as Amawbia = 8.3%, Zik's Avenue = 13.9%, Amaikwo = 8.8%, Amaenyi/Amaku = 8.6%, Udoka Estate = 8.8%, Nibo/Umuawulu = 7.2%, Iyiagu Estate = 6.4%, Okpuno = 8.2%, Enugu/Onitsha Expressway = 6.3%, Emma Nnaemeka Axis = 5.2%, Ifite = 9.2% and Government House = 9.1%. The multiple regression analysis was used to investigate how solid wastes generated from each of these twelve constituting zones of Awka Municipality combined to affect the total quantities generated monthly. Results of the study have also proven integrity of addressing 85% of ASWAMA problems in providing sustainable quality services in the state. In conclusion, appropriate models for enhancing the performance of the Anambra State Waste Management Agency have been developed in this study. In particular, the models are for determining the quantities of waste generated and the quantities that should be evacuated to maintain minimal inventory of waste in a given location of the study area. They also facilitate methods of estimating the costs of waste evacuation as well as evaluating a waste manager's performance. Consequently, it is recommended that waste managers in Anambra State or any other state should adopt the developed models as tools in their solid waste inventory management.