Thermal treatment

The purpose of thermal treatment

 

Thermal treatment aims to add value to solids or reduce environmental pollution. Incineration, gasification, pyrolysis and torrefaction are examples of thermal treatments. Modern incinerators effectively dispose of household waste. However, their cost is unaffordable for most of the inhabitants of the planet and incinerators do not fully exploit the potential for energy recovery and recycling of waste.

Torrefaction for biomass upgrading

 

Torrefaction is a thermal treatment process that could help to sustainably exploit woody biomass resources to provide heat and power. In many countries, a large quantity of garden trimmings are collected, centralized and burned or transformed into compost. Additionally, large quantities of forest residues are available. Due to the lack of a suitable transformation process, these resources are underexploited and in the case of garden trimmings their management is a cost to the local communities. Moreover, the fact that heating is necessary only during the winter results in a lack of demand and a biomass storage problem during the summer months. Torrefaction has been proposed as a solution to upgrade low quality woody residues to energy dense solid fuel for conventional wood burning systems.

 

Appropriate treatment of wet municipal solid wastes

 

The trend in wealthy urban areas with cold climates is towards waste sorting, incineration, combined heat and power and district heating. Due to the high water and organic content of municipal solid wastes in middle and lower income countries, the relative difficulty of sorting, scarcity of electric power and the low demand for heat, this standard waste treatment system is not appropriate for treating the solid wastes generated in large middle and lower income urban areas in the tropical and subtropical climate zones. A novel system combining pyrolysis and anaerobic digestion might be more effective economically and energetically.

Optimization of heat treatment processes using neural networks

 

Due to the inhomogeneity of the inputs and the complex kinetics of the reactions, the modelling of thermal treatment processes is difficult. Our understanding of heat treatment processes could benefit from an approach to modeling and simulation based on tools from the fields of machine learning and artificial neural network predictive models. Thus, the optimizing the technical and economic operation of a household waste incineration plant or plant sub-processes could benefit from a modeling approach based on neural networks.

With this goal, Yang et. al.¹ developed an artificial neural network capable of predicting energy efficiency and particle collection efficiency as a function of operating voltage, current, residence time, and gas temperature of an electrostatic precipitation (ESP) unit. The root mean square error of the predictions was 0.0027%.

 

1) Zhengda Yang, Yuxiang Cai, Qingyi Li, Hanqing Li, Ye Jiang, Riyi Lin, Chenghang Zheng, Deshan Sun, Xiang Gao. Predicting particle collection performance of a wet electrostatic precipitator under varied conditions with artificial neural networks, Powder Technology, Volume 377, 2021, Pages 632-639.