Application of poly aluminum chloride and alum as catalyst in catalytic ozonation process after coagulation for the treatment of textile wastewater.

Textile wastewater is ranked highly contaminated among all industrial waste. During textile processing, the consumption of dyes and complex chemicals at various stages makes textile industrial wastewater highly challenging. Therefore, conventional processes based on single-unit treatment may not be sufficient to comply with the environmental quality discharge standards and more stringent guidelines for zero discharge of hazardous chemicals (ZDHC). In this study, a novel approach was followed by recycling Poly aluminum chloride (PACl) and Alum as a catalyst for the first time in the catalytic ozonation treatment process leading to a nascent method after using them as a coagulant in Coagulation/Flocculation. In the current investigation, six different combinations were studied to remove turbidity, TSS, COD, BOD5, color, and biodegradability (BOD5/COD ratios) of wastewater. Moreover, Central Composite Design was implied using RSM in Minitab software. During the combination of treatment processes, it was found that the pre-coagulation/flocculation with coagulant PACl followed by post-catalytic ozonation with recycled PACl, a more effective treatment than others. The optimum R.E of turbidity, TSS, COD, and color were 84%, 86%, 89%, and 98%, respectively. Moreover, a decrease in toxicity and increase in biodegradability (BOD5/COD ratio from 0.29 to 0.54) was observed as well. The electrical energy demand and operational costs of treatment processes were estimated and compared with other treatment processes.

Magnetocaloric effect modeling of dysprosium-transition metal based intermetallic alloys for magnetic refrigeration application using hybrid genetic algorithm based support vector regression intelligent method.

Intermetallic alloy containing rare earth dysprosium ions with the associated unfilled 4f shell electrons and sub-lattice of 3d-transition metal, results into fascinating magnetic properties which are useful for green refrigeration technological application. Magnetocaloric effect remains the fundamental principle upon which magnetic refrigeration technology is based while this cooling technology has advantages of cost effectiveness, high efficiency and environmental friendliness as compared with the existing conventional gas compression systems. Maximum magnetic entropy change (which controls the hugeness of magnetocaloric effect) of intermetallic alloy Dy-T-X (where T = transition metal and X = any other metal or nonmetal) is modeled in this work using hybrid genetic algorithm based support vector regression (GSVR) computational intelligent method with applied magnetic field, ionic concentration and ionic radii descriptors. The developed GSVR-G model with kernel Gaussian function outperforms GSVR-P model with polynomial function with improvement of 85.23%, 78.82% and 78.67% on the basis of the computed correlation coefficient (CC), mean absolute error (MAE) and root mean square error (RMSE) on testing sample, respectively. The developed model further investigates the influence of applied external magnetic field on magnetocaloric effect of DyCuAl intermetallic alloy. The developed models in this work circumvent experimental challenges of magnetocaloric effect determination while the recorded precision of the developed model further opens doors for possible exploration of these intermetallic compounds for addressing environmental challenges associated with the present system of cooling.

A hybrid smell agent symbiosis organism search algorithm for optimal control of microgrid operations.

This paper presents a hybrid Smell Agent Symbiosis Organism Search Algorithm (SASOS) for optimal control of autonomous microgrids. In microgrid operation, a single optimization algorithm often lacks the required balance between accuracy and speed to control power system parameters such as frequency and voltage effectively. The hybrid algorithm reduces the imbalance between exploitation and exploration and increases the effectiveness of control optimization in microgrids. To achieve this, various energy resource models were coordinated into a single model for optimal energy generation and distribution to loads. The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. The development of SASOS comprises components of Symbiotic Organism Search (SOS) and Smell Agent Optimization (SAO) codified in an optimization loop. Twenty-four standard test function benchmarks were used to evaluate the performance of the algorithm developed. The experimental analysis revealed that SASOS obtained 58.82% of the Desired Convergence Goal (DCG) in 17 of the benchmark functions. SASOS was implemented in the Microgrid Central Controller (MCC) and benchmarked alongside standard SOS and SAO optimization control strategies. The MATLAB/Simulink simulation results of the microgrid load disturbance rejection showed the viability of SASOS with an improved reduction in Total Harmonic Distortion (THD) of 19.76%, compared to the SOS, SAO, and MCC methods that have a THD reduction of 15.60%, 12.74%, and 6.04%, respectively, over the THD benchmark. Based on the results obtained, it can be concluded that SASOS demonstrates superior performance compared to other methods. This finding suggests that SASOS is a promising solution for enhancing the control system of autonomous microgrids. It was also shown to apply to other sectors of engineering optimization.