A heating value estimation of refuse derived fuel using the genetic programming model

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).

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A new genetic programming model for predicting settlement of shallow foundations

Canadian Geotechnical Journal ◽

10.1139/t07-063 ◽

2007 ◽

Vol 44 (12) ◽

pp. 1462-1473 ◽

Cited By ~ 66

Author(s):

Mohammad Rezania ◽

Akbar A. Javadi

Keyword(s):

Genetic Programming ◽

Programming Model ◽

Standard Penetration Test ◽

Shallow Foundations ◽

Penetration Test ◽

Case Histories ◽

Contributing Factors ◽

Artificial Neural Network Ann ◽

Gp Model ◽

Very High

In this paper, a new genetic programming (GP) approach for predicting settlement of shallow foundations is presented. The GP model is developed and verified using a large database of standard penetration test (SPT) based case histories that involve measured settlements of shallow foundations. The results of the developed GP model are compared with those of a number of commonly used traditional methods and artificial neural network (ANN) based models. It is shown that the GP model is able to learn, with a very high accuracy, the complex relationship between foundation settlement and its contributing factors, and render this knowledge in the form of a function. The attained function can be used to generalize the learning and apply it to predict settlement of foundations for new cases not used in the development of the model. The advantages of the proposed GP model over the conventional and ANN based models are highlighted.

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Characterization of Refuse Derived Fuel Production from Municipal Solid Waste: The Case Studies in Latvia and Lithuania

Environmental and Climate Technologies ◽

10.2478/rtuect-2020-0090 ◽

2020 ◽

Vol 24 (3) ◽

pp. 112-118

Author(s):

Dace Âriņa ◽

Rūta Bendere ◽

Gintaras Denafas ◽

Jānis Kalnačs ◽

Mait Kriipsalu

Keyword(s):

Municipal Solid Waste ◽

Solid Waste ◽

Calorific Value ◽

Ash Content ◽

Fuel Production ◽

Heating Value ◽

Refuse Derived Fuel ◽

Morphological Composition ◽

The Mean

AbstractThe authors determined the morphological composition of refuse derived fuel (RDF) produced in Latvia and Lithuania by manually sorting. The parameters of RDF (moisture, net calorific value, ash content, carbon, nitrogen, hydrogen, sulphur, chlorine, metals) was determined using the EN standards. Comparing obtained results with data from literature, authors have found that the content of plastic is higher but paper and cardboard is lower than typical values. Results also show that the mean parameters for RDF can be classified with the class codes: Net heating value (3); chlorine (3); mercury (1), and responds to limits stated for 3rd class of solid recovered fuel. It is recommended to separate biological waste at source to lower moisture and ash content and increase heating value for potential fuel production from waste.

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