Abstract. Neural network based experimental and hybrid approaches to modeling of processes are presented. A hybrid modeling, combining parametric model with radial basis function network, is proposed. The parametric model is used for principal modeling of the process and the radial basis function network is applied for nonlinear error correction. Experimental modeling can be improved by selecting as model inputs only variables with high predictive importance. Two feature selection methods are presented: analysis of mutual information, and genetic algorithm based feature selection. The methods proposed are applied to modeling of the liquid-phase methanol synthesis. Analytical, experimental and hybrid approaches are applied and results demonstrate that a hybrid modeling approach, exploiting available analytical knowledge and experimental data, can considerably outperform a purely analytical approach.
Municipal waste largely consists of carbon, hydrogen and oxygen. Other elements are represented in smaller proportions. Mostly, this type of waste ends up in the incinerator or landfill. Re-use is limited due to the contamination of the raw material. On paper, the model is presented that enables step by step conversion of waste into synthesis gas, which is a raw material for methanol production and production of other hydrocarbons, through the gasification system. This method allows endless re-use of material, with part of the raw material being converted into energy, carbon dioxide and water. On paper, the design of the model and the efficiency of the process is presented.
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