Masonry compressive strength prediction using artificial neural networks

The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of masonry has been investigated. Specifically, back-propagation neural network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of masonry walls in a reliable and robust manner.- (undefined

Energy recovery from solid waste: Application of gasification technology

Rasul, M ORCiD: 0000-0001-8159-1321Solid waste is considered either as a burden or as a valuable resource for energy generation. Identifying an environmentally sound and techno-economically feasible solid waste treatment is a global and local challenge. This chapter focuses on application of gasification technology to produce energy from solid waste. The study was done both experimentally and numerically. Experimental investigation of solid waste gasification was performed using a pilot-scale gasification plant. In this experiment, wood chips were used as feedstock (solid waste) under specified gasifier operating conditions. Syngas composition was measured at different stages of gasification, such as raw, scrubbed, and dewatered syngas. Mass and energy balance was analyzed using the experimentally measured data. A computational model was developed using Aspen Plus software for the fluidized bed gasification process through Gibbs free energy minimization approach. The model was validated with experimental data. The validated model was then used to predict the various operating parameters of a solid waste gasification plant, such as temperature, pressure, air-fuel ratio, and steam-fuel ratio. This study broadly focuses on the area of energy and the environment through detailed investigation of solid waste including municipal solid waste, municipal green waste, and agricultural solid waste management and energy from waste technologies. The findings of this study contribute to better understanding of the benefits and applications of gasification technology for energy recovery. Policy and decision makers at national and international levels, who are concerned with developing environmentally friendly waste management technologies, will benefit from the outcomes of this study. © Springer Nature Switzerland AG 2019. All Rights Reserved

A Feasible Application of Circular Economy: Spent Grain Energy Recovery in the Beer Industry

The generation of residual streams and wastes is a common constant in all productive processes. The brewing sector generates a large quantity of residual by-products which can be sustainably reused within the industry to contribute to cover the energy requirement of the process and at the same time to contribute to minimize the amount of waste that is sent to landfills. In this paper the feasibility and advantages of incorporating a stage for energy recovery from some of the solid wastes generated during the process as part of the circular economy approach is presented. La Cibeles, a local small size beer process is taken as a real example. In a brewing process the main wastes that are produced are: grain husks, yeast and CO2. Out of the three, the most important one is the grain husk or brewers??? spent grain that can make around 85% of the total waste of a brewery. The results presented in this study show that, by gasification of brewers??? spent grain, not only the final volume of the residue to be disposed is considerably minimised, but also it is possible to obtain a net economic saving of around 22% in the consume of fossil fuels used in the brewing process when the syngas produced is used for heat generation