Municipal solid waste higher heating value prediction from ultimate analysis using multiple regression and genetic programming techniques.

Municipal solid waste (MSW) management presents an important challenge for all countries. In order to exploit them as a source of energy, a knowledge of their calorific value is essential. In fact, it can be experimentally measured by an oxygen bomb calorimeter. This process is, however, expensive. In this light, the purpose of this paper was to develop empirical models for the prediction of MSW higher heating value (HHV) from ultimate analysis. Two methods were used: multiple regression analysis and genetic programming formalism. Both techniques gave good results. Genetic programming, however, provides more accuracy compared to published works in terms of a great correlation coefficient (CC) and a low root mean square error (RMSE).

Waste-to-energy as a tool of circular economy: Prediction of higher heating value of biomass by artificial neural network (ANN) and multivariate linear regression (MLR).

Circular economy is a global trend as a promising strategy for the sustainable use of natural resources. In this context, waste-to-energy presents an effective solution to respond to the ever-increasing waste generation and energy demand duality. However, waste diversity makes their management a serious challenge. Among their categories, biomass waste valorization is an attractive solution energy regarding its low cost and raw materials availability. Nevertheless, the knowledge of biomass waste characteristics, such as composition and energy content, is a necessity. In this research, new models are developed to estimate biomass wastes higher heating value (HHV) based on the ultimate analysis using linear regression and artificial neural network (ANN). The quality-measure of the two models for new dataset was evaluated with statistical metrics such as coefficient of correlation (R), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The methods developed in this work provided attractive accuracies comparing to other literature models. Additionally, it is found that the ANN, as machine learning method, is the best model for biomass HHV prediction (R = 0.75377, RMSE = 1.17527, MAE = 0.93315 and MAPE = 5.73%). Therefore, obtained results can be widely employed to design and optimize the reactors of combustion. In fact, the developed ANN software is a simple and accurate tool for HHV estimation based on ultimate analysis. Indeed, ANN is one of the most applicable and widely used software in the field of waste-to-energy.

Investigation of (co)-combustion kinetics of biomass, coal and municipal solid wastes.

Investigation of thermal behaviors of biomass waste, biochar, coal, municipal solid waste (MSW) and their mixtures were aimed in the present study using both thermogravimetric analysis and differential scanning calorimeter techniques. In fact, this paper intends to interpret the influence of mixtures on activation energy. In this purpose, Coats and Redfern were used. Then, the relative error Δmerror was calculated to quantify the synergism degree. Precisely, it was about 5.34% for biomass/coal, 5.52% for biomass/cardboard, 5.67% for biomass/biochar, 5.93% for biomass/synthetic rubber and 6.05% for biomass/plastic mixtures. This phenomenon was justified by the interaction between C-C bond of biochar, coal and MSW radicals with C-H and C-O bonds of biomass.

Effect of materials mixture on the higher heating value: Case of biomass, biochar and municipal solid waste.

The heating value describes the energy content of any fuel. In this study, this parameter was evaluated for different abundant materials in Morocco (two types of biochar, plastic, synthetic rubber, and cardboard as municipal solid waste (MSW), and various types of biomass). Before the evaluation of their higher heating value (HHV) by a calorimeter device, the thermal behavior of these materials was investigated using thermogravimetric (TGA) and Differential scanning calorimetry (DSC) analyses. The focus of this work is to evaluate the calorific value of each material alone in a first time, then to compare the experimental and theoretical HHV of their mixtures in a second time. The heating value of lignocellulosic materials was between 12.16 and 20.53MJ/kg, 27.39 for biochar 1, 32.60MJ/kg for biochar 2, 37.81 and 38.00MJ/kg for plastic and synthetic rubber respectively and 13.81MJ/kg for cardboard. A significant difference was observed between the measured and estimated HHVs of mixtures. Experimentally, results for a large variety of mixture between biomass/biochar and biomass/MSW have shown that the interaction between biomass and other compounds expressed a synergy of 2.37% for biochar 1 and 6.11% for biochar 2, 1.09% for cardboard, 5.09% for plastic and 5.01% for synthetic rubber.