N. E. Ahmad Basri

Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model

Solid waste prediction is crucial for sustainable solid waste management. The collection of accurate waste data records is challenging in developing countries. Solid waste generation is usually correlated with economic, demographic and social factors. However, these factors are not constant due to population and economic growth. The objective of this research is to minimize the land requirements for solid waste disposal for implementation of the Malaysian vision of waste disposal options. This goal has been previously achieved by integrating the solid waste forecasting model, waste composition and the Malaysian vision. The modified adaptive neural fuzzy inference system (MANFIS) was employed to develop a solid waste prediction model and search for the optimum input factors. The performance of the model was evaluated using the root mean square error (RMSE) and the coefficient of determination (R(2)). The model validation results are as follows: RMSE for training=0.2678, RMSE for testing=3.9860 and R(2)=0.99. Implementation of the Malaysian vision for waste disposal options can minimize the land requirements for waste disposal by up to 43%.

Electronic plastic waste management in malaysia: the potential of waste to energy conversion

The production of electronic products is one of the worlds fasting growing industries today. Due to this phenomena, the amount of electronic waste generated increases proportionately with the production. The growing numbers of uses of plastic products in this sector contribute to electronic plastic waste generated. From the management of solid waste aspect, the production of electronic plastic waste has to be handled effectively. Generally, there are three options for electronic plastic waste recycling which is chemical, mechanical or thermal recycling. The main purpose of this paper is to discuss the research conducted on electronic plastic waste which consisted of various types of resin to determine the electronic plastic wastes potential as a source of energy. The physical and chemical characteristics of the electronic plastic waste are determined by the proximate analysis, ultimate analysis and the heavy metal content analysis of the plastic waste resin sample. The dulong formula was applied to calculate the heating value of electronic plastic waste based on the data obtained from the ultimate analysis. The result shows that the average heat value for an electronic waste is 30, 872.42 kj/kg or 7, 375 kcal/kg. The emmission factor analysis shows the concentration of air emission value which would probably be formed due to incineration activitiy is less than the effluent parameter of standard A and standard B limits fixed by the environmental quality act (clean air) 1978 for control air pollution. Basically, this research has succeeded in proving the potential of electronic plastic waste to be used as a source of energy in the future.

Solid waste forecasting using modified ANFIS modeling

Solid waste prediction is crucial for sustainable solid waste management. Usually, accurate waste generation record is challenge in developing countries which complicates the modelling process. Solid waste generation is related to demographic, economic, and social factors. However, these factors are highly varied due to population and economy growths. The objective of this research is to determine the most influencing demographic and economic factors that affect solid waste generation using systematic approach, and then develop a model to forecast solid waste generation using a modified Adaptive Neural Inference System (MANFIS). The model evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R2). The results show that the best input variables are people age groups 0-14, 15-64, and people above 65 years, and the best model structure is 3 triangular fuzzy membership functions and 27 fuzzy rules. The model has been validated using testing data and the resulted training RMSE, MAE and R2 were 0.2678, 0.045 and 0.99, respectively, while for testing phase RMSE =3.986, MAE = 0.673 and R2 = 0.98. Implications: To date, a few attempts have been made to predict the annual solid waste generation in developing countries. This paper presents modeling of annual solid waste generation using Modified ANFIS, it is a systematic approach to search for the most influencing factors and then modify the ANFIS structure to simplify the model. The proposed method can be used to forecast the waste generation in such developing countries where accurate reliable data is not always available. Moreover, annual solid waste prediction is essential for sustainable planning.

An application of anaerobic baffled reactor to produce biogas from kitchen waste

The tremendous increase in solid waste generation is an unavoidable occurrence due to the fast growing urbanisation and industrialisation in Malaysia. Anaerobic digestion of organic wastes is receiving more attention in recent years throughout the world because the biomethanogenesis process decomposes organic matter to produce methane gas, which is an excellent energy source as fuel in combined heat and power units. In this study an application of an Anaerobic Baffled Reactor (ABR) for the production of biogas from kitchen waste was carried out to identify the optimum efficiency of methane gas generation and the potential usage of sludge as organic fertiliser. Different proportions of kitchen waste and activated sewage sludge were mixed and tested in the reactor to achieve the best amount of methane production in the shortest time. Results showed that the combination of 75% of kitchen waste and 25% of activated sewage sludge presented as the best result, which was 74.1% of methane gas. Further, determination for fertiliser value from tests on the sludge in the reactor showed its potential for future use in composting. The amounts of N, P and K were 0.95, 0.80 and 0.45% respectively. According to the observation, anaerobic digestion of kitchen waste in the ABR is able to provide a vital element in an integrated solid waste management and the energy production from this system could be a good reason for many communities to start recycling valuable resources, and hence achieving zero waste production.

Priorities determination using novel analytic hierarchy process and median ranked sample set, case study of landfill siting criteria

Integrating environmental, social, political, and economical attributes enhances the decision making process. Multi criteria decision making (MCDM) involves ambiguity and uncertainty due to various preferences. This study presents a model to minimize the uncertainty and ambiguity of human judgments by means of integrating the counter stakeholders with median ranked sample set (MRSS) and Analytic hierarchy process (AHP). The model uses landfill site selection as a MCDM problem. Sixteen experts belong to four clusters that are government, private, institution, and non-governmental organisations participated and their preferences were ranked in four by four matrix. Then the MRSS and the AHP were used to obtain the priorities of landfill siting criteria. Environmental criteria have the highest priority that equals to 48.1% and the distance from surface water, and the faults zones are the most important factors with priorities equal to 18% and 13.7% respectively. In conclusion, the hybrid approach that integrates counter stakeholders MRSS, and AHP is capable of being applied to complex decision making process and its outputs are justified.

Use of a Combination of MRSS-ANP for Making an Innovative Landfill Siting Decision Model

Landfill siting is a complex, multicriteria decision-making problem that needs an extensive evaluation of environmental, social, land use, and operational criteria. Integration of a median ranked sample set (MRSS) and an analytic network process (ANP) has been implemented to rank the associated criteria and select a suitable landfill site. It minimizes the uncertainty and the subjectivity of human judgments. Four groups of experts with different backgrounds participated in this study, and each group contained four experts. The respondent preferences were ranked in a 4-by-4 matrix to obtain the judgment sets for the MRSS. These sets were subsequently analyzed using ANP to obtain the priorities in the landfill siting criteria. The results show that land topology and distance from surface water are the most influential factors, with priorities of 0.18 and 0.17, respectively. The proposed integrated model may become a promising tool for the environmental planners and decision makers.

Prediction of municipal solid waste generation using nonlinear autoregressive network.

Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R2) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R2 (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell’s conjugate gradient as the training algorithm.

The application of an anaerobic baffled reactor to produce biogas from kitchen waste

The tremendous increase in solid waste generation is an unavoidable occurrence due to the fast growing urbanisation and industrialisation in Malaysia. Anaerobic digestion of organic wastes is receiving more attention in recent years throughout the world because the biomethanogenesis process decomposes organic matter to produce methane gas, which is an excellent energy source as fuel in combined heat and power units. In this study, an application of an anaerobic baffled reactor (ABR) for the production of biogas from kitchen waste was carried out to identify the optimum efficiency of methane gas generation and the potential usage of sludge as organic fertiliser. Different proportions of kitchen waste and activated sewage sludge were mixed and tested in the reactor to achieve the best amount of methane production in the shortest time. Results showed that the combination of 75% of kitchen waste and 25% of activated sewage sludge yielded the best result, which was 74.1% of methane gas. Further, determination for fertiliser value from tests on the sludge in the reactor showed its potential for future use in composting. The amounts of N, P and K were 0.95, 0.80 and 0.45% respectively. According to the observation, anaerobic digestion of kitchen waste in the ABR is able to provide a vital element in an integrated solid waste management and the energy production from this system could be a good reason for many communities to start recycling valuable resources and hence achieving zero waste production.