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The present study employs the S/N ratio and multiple regression analysis to evaluate the mechanical properties of laterized concrete with fly ash and steel fiber. The study considers compressive, split tensile, and flexural strengths as responses at 7 and 28 days. The Taguchi L16 (4 *4) orthogonal array experimental design is applied to optimize the performance of mechanical properties of concrete by using the design of experiments. The replacement of 25% of the granite aggregate with laterite aggregate resulted in a 12.44% increase in the strength of M30-grade concrete after 28 days. The composite's compressive strength decreases when the replacement level exceeds 25%. The split tensile and flexural strengths of the composite showed an increase of 14.54% and 16.93%, respectively, compared to the control mix. Furthermore, the test outcomes for durability factors like drying shrinkage and rapid chloride penetration in steel fiber reinforced fly ash-based lateritic concrete mixes with a 50% replacement rate adhere to the prescribed standards, verifying their designed performance over the designated structural lifespan. However, after multiple regression analysis, it is found that the error related to the correlation between experimental and analytical value strength is about 5%. Microstructural studies of the ideal mix were performed at 28 days to further understand the morphology of the laterized concrete containing fly ash and steel fiber. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UASNs) are quickly being used in environmental monitoring, marine life observation, and underwater resource research. This paper introduces the Dingo Optimisation Influenced-Arithmetic Clustering and Localisation Algorithm. The proposed method was developed due to the above considerations. DOIAO-CLA combines Dingo Optimisation (DO) and Arithmetic Optimisation (AO), two powerful optimisation methods. DOIAO-CLA uses DO's robust exploration and exploitation features to group sensor nodes with similar propagation characteristics. It also optimises cluster formation and reduces distance discrepancies using AO. DOIAO-CLA also enables reliable localization by merging distance- and angle-based methods. DO optimises node localization by using distance and angle of arrival data. This integration enhances UASN node localization accuracy and reliability. The DOIAO-CLA was tested against other clustering and localization algorithms like Archimedes Optimisation Algorithm (AOA), Squirrel Search Algorithm (SSA), Hybrid Cat Cheetah Optimisation Algorithm (HCCOA), Ant Colony Optimisation Algorithm (ACOA), Grey Wolf Optimisation Algorithm (GWOA), and Particle Swarm Optimisation Algorithm. According to the results, DOIAO-CLA taps alternatives for network robustness, data aggregation, and localization precision. Finally, DOIAO-CLA solves the UASN clustering and localization problem in a novel and effective way. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms are stochastic that reflects the biological evolution to reach optimal solutions to optimization problems where mathematical techniques may fail. Demand Side Management (DSM) are designed to reduce electricity consumption or to shift the consumption from peak to off - peak hours depending on consumers' lifestyle and behaviour. DSM is a flexible consumer driven activity in which the consumer has voluntarily changed his energy usage pattern during peak demand so to maintain the reliability and stability of power system and the performance of an electrical grid. In this aspect we have explored the impact of an efficient and flexible DSM which can reduce the power demand and energy in different areas like rural, urban and villa there by utilizing the device power rating and its activation time. The defined problem was solved with evolutionary based - genetic (GA), particle swarm (PSO) and differential evolution (DE) optimization algorithms. Simulation results show the better outcome in terms of power demand and energy reduction and the results are compared to know the better performing algorithm as on applied to DSM. [ABSTRACT FROM AUTHOR]

The decolourization efficiency of azo dye, Congo red was examined by heterogeneous Fenton reaction using different support like zeolite, activated carbon and fly ash. The iron catalyst was coated into the support by wet impregnation method. Among different support system, zeolite supported iron catalyst (Fe-Z) exhibited maximum decolourization which was used for further studies. The prepared catalyst (Fe-Z) was characterized by scanning electron microscope integrated with energy dispersive x-ray spectroscopy. Experiments were conducted in a batch reactor to examine the effect of operating variables like pH, peroxide concentration [H 2 O 2 ] and ferrous dosage [Fe 2+ ] in zeolite governing the process. The optimum reaction conditions were found to be [H 2 O 2 ] = 4 mM, [Fe-Z] = 0.4 mM for 0.861 mM of Congo red at pH 3.0 ± 0.2. The process follows a pseudo first order kinetics with a rate constant of 0.012 min −1 (R 2 > 0.9). The oxidation products of the heterogeneous system were identified by gas chromatography integrated with mass spectrometry (GC-MS) and the plausible mechanistic pathway was proposed. The results prove that the Fe-Z could be a effective catalyst in decolourizing the Congo red at room conditions. [ABSTRACT FROM AUTHOR]

The main purpose of this study to increase the optimal conditions for biogas yield from anaerobic digestion of agricultural waste (Rice Straw) using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). In the development of predictive models temperature, pH, substrate concentration and agitation time are conceived as model variables. The experimental results show that the liner model terms of temperature, substrate concentration and pH, agitation time have significance of interactive effects ( p < 0.05). The results manifest that the optimum process parameters affected on biogas yield increase from the ANN model when compared to RSM model. The ANN model indicates that it is much more accurate and reckons the values of maximum biogas yield when compared to RSM model. [ABSTRACT FROM AUTHOR]