Electrical power prediction through a combination of multilayer perceptron with water cycle ant lion and satin bowerbird searching optimizers

Predicting the electrical power (PE) output is a significant step toward the sustainable development of combined cycle power plants. Due to the effect of several parameters on the simulation of PE, utilizing a robust method is of high importance. Hence, in this study, a potent metaheuristic strategy, namely, the water cycle algorithm (WCA), is employed to solve this issue. First, a nonlinear neural network framework is formed to link the PE with influential parameters. Then, the network is optimized by the WCA algorithm. A publicly available dataset is used to feed the hybrid model. Since the WCA is a population-based technique, its sensitivity to the population size is assessed by a trial-and-error effort to attain the most suitable configuration. The results in the training phase showed that the proposed WCA can find an optimal solution for capturing the relationship between the PE and influential factors with less than 1% error. Likewise, examining the test results revealed that this model can forecast the PE with high accuracy. Moreover, a comparison with two powerful benchmark techniques, namely, ant lion optimization and a satin bowerbird optimizer, pointed to the WCA as a more accurate technique for the sustainable design of the intended system. Lastly, two potential predictive formulas, based on the most efficient WCAs, are extracted and presented

An innovative metaheuristic strategy for solar energy management through a neural networks framework

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development

Enhancing electrokinetic environment to improve physicochemical properties of kaolinite using polyvinyl alcohol and cement stabilizers

Adsorption of polymers on clay and flocculation of clay by polymers in presence of cement were studied in order to understand clay polymer interactions. Also, findings into the stabilization effect of the kaolinite that was mixed with various binders to form a stabilized soil are presented. Special attention was focused on two types of PVA: fully (PVA-F) and partially (PVA-P) hydrolyzed with varying degrees of concentration. Although, increasing polymer concentration in both PVA-F and PVA-P samples enhanced physicochemical results, PVA-F showed higher improvement than PVA-P. As a result, Unconfined compressive strength (UCS) of stabilized kaolinite increased as high as 5 to 109 times comparing with untreated kaolinite. According to 28 days curing time, the optimum dose of PVA was also evaluated 3gr/L and 1gr/L for PVA-P and PVA-F, respectively. Although, pH at isoelectric point was between 3.1 and 3.2, isoelectric point of kaolinite immersed in PVA solution observed at pH between 1.9 and 2.1

Strain absorption optimization of reinforcement on geosynthetic reinforced slope: experimental and FEM modeling

The interaction between shear plane and Geosynthetic reinforcement in reinforced slope were carried out by using direct shear test apparatus. The reinforced soil specimens were installed through the large shear box in five different systems including 0, 30, 45, 60 and 90 degrees of reinforcement orientation with respect to the vertical axis in shear box. Results showed maximum shear results when interaction angle were between 45 to 60 degrees. In such angle of reinforcement orientation strain absorption showed its maximum values in all effective vertical axes of 50, 100 and 200 kPa, which mean the maximum shear tension, observed. This optimization can be due to better interaction between both coarse granular soils with Geogrid apertures which eventually give rise to more tension stresses absorption of reinforcement through the reinforced soil

Groundwater quality assessment of Labuan Island using GIS

Groundwater has been considered as an important source of water supply due to its relatively low susceptibility to pollution in comparison to surface water, and its large storage capacity (US EPA 1985). It should be mentioned that water pollution is any chemical, physical or biological change in the quality of water that has a harmful effect on any living thing that drinks or uses or lives (in) it. When humans drink polluted water it often has serious consequences on their health. Water pollution can also make water unsuited for the desired use. In present study different water quality parameters were observed from the different monitoring wells through the Labuan Island. Then by interpolation between the available data different water quality parameters maps were created. Each map is classified based on the Malaysia water quality standards. Results show those areas which are susceptible to groundwater contamination

Double-target based neural networks in predicting energy consumption in residential buildings

A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation = 0.977 and RMSE error = 0.183) and testing (R2 correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R2 correlation = 0.99 and RMSE error = 0.147) and testing (R2 correlation = 0.99 and RMSE error = 0.148))

Synthesizing multi-layer perceptron network with ant lion biogeography-based dragonfly algorithm evolutionary strategy invasive weed and league champion optimization hybrid algorithms in predicting heating load in residential buildings

The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis

Optimization of Tension Absorption of Geosynthetics through Reinforced Slope

A series of direct shear tests were carried out on a sandy soil to evaluate the effect of optimum reinforcement orientation through the geosynthetic reinforced slopes. The main aim of the research was to find out how reinforcement should be installed through the shear box with respect to shear plane and regarding to absorbing maximum tension forces. For finding this optimization angle, a large-scale direct shear box was used. The reinforced soil specimens (30.4 × 30.4 × 15.0 cm) were installed through the shear box in five different forms including 0, 30, 45, 60, 90 degree of reinforcement respect to vertical axis of shear box. Based on the results gained from the research, the optimization angle of reinforcement that both shear displacement and soil dilation have caused the most tension stress in dry sandy soils was between 45 and 60 degree with respect to shear failure plane. It was because of the better interaction of the coarse granular soils with geogrid apertures. Finally, a series of two-dimensional finite element simulations were carried out using the results of optimum direction to describe the behavior of reinforced slope under different reinforcement directions with respect to shear failure plane

Zeta potential of organic soil in presence of calcium chloride, cement and polyvinyl alcohol

In this study the influence of varoius dosage of cement, polyvinyl alcohol (PVA), and calcium chloride (CaCl2) on the zeta potential of organic soil has been examined. Two different PVA species were used, fully hydrolyzed (PVA-F) as well as partially hydrolyzed (PVA-T). As results, adding the PVA and cement dosage into the suspended colloids led to an increase of zeta potential in their surfaces, contrary to measuring done in water. In absence of CaCl2, zeta potential of organic soil immersed in PVA or cement showed a range between +22 to +211 mV at pH ~ 1.7 to 11.3, while, in presence of CaCl2 the variation of zeta potential was in a range of +25 to -110mV at pH ~2.2 to 10.3. Although, there was no IEP in presence of CaCl2 additives, a peak in zeta potential was observed for organic soil immersed in various electrolytes. Moreover, iso-electric point (IEP), for soil samples suspended in water is at pH about 3.1 to 3.3. However, the IEP of organic soil when is suspended in cement and/or PVA solution significantly decrease to the values about pH~1.9 to 2.0