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The extraordinary amount of solar irradiative energy all around the Earth has one of the motivational factors to work with such systems that utilize these for the services towards the benefits of mankind and its progressive growth, and that makes a powerful society and commendable nation. However, in the current era an unavoidable problem for the scarcity of potable water is going on, as brackish water keeps increasing faster than the approachable fresh water resources. For this, solar still systems for water treatment are one of the best solutions that utilize the solar energy as a prime source of working to the system. Out of those systems, energy matrix is one of the important performing factors that indicate the overall usability, payout characteristics, life cycle performing efficiency, etc. of the system. The current presentation reflects that active parameters undertaken in previous studies especially based on the energy matrix for active solar stills. A comparative result has been shown that results better in favor of nanoparticles and lower embodied energetic systems. Based on the overall performances and influencive parameters, a discussion and focused concluding remarks has been framed that suitably justifies the development towards this technology.

Solar clean energy can be harnessed by several methods using technologies like solar heating, photovoltaic cell, solar architecture, photosynthesis. Solar energy is converted either by active techn...

In recent years, sunlight has been used in several fields such as photovoltaic cells, flat plate collectors, solar cookers, green buildings, and agricultural applications. Improved thermal performance has been seen which comes of three sides absorber plate with glass cover compared to the traditional one. This paper presents the Nusselt (Nu) number, collector efficiency factor (CEF), and collector heat removal factor (CHRF) for the optimal solution of three sides artificially roughened solar air heater. Five input variables such as Reynolds (Re) number, relative roughness pitch, relative roughness height, mass flow rate, and air temperature of the duct are taken into account for improved efficiency optimization of collector, collector heat removal factor, and Nu number. Technique for order of preference by similarity to ideal solution (TOPSIS) technique is used to identify the best alternative amongst a number of performance measures by converting them into an equivalent single variable. Moreover, the results revealed the high accuracy of the CEF, CHRF, and Nu number of 75–80%, 74–78%, and 63–71%, respectively. Meanwhile, it has been also observed that roughness Re number varies between 12,500 and 13,500, and height of relative roughness is 0.0245, including pitch of relative roughness 10 along with the rate of mass flow is 0.041 kg/s.

In order to machine hard and high-strength-to-weight ratio materials, electrical discharge machining (EDM) process is extensively used in aerospace, automobile and other industrial applications. However, high erosion of tool and improper selection of machining variables have emerged as a major obstruction to achieve productivity in this direction. High erosion of tool not only enhances the cost of machining but also increases the machining time by causing interruption during machining. Therefore, proper selection of machining variables and tool material life are the two vital aspects for the tool engineers working in EDM. In view of this, the present work proposes an extensive experimental investigation and optimization of machining variables of cryogenically treated brass tool materials on machining competences of Inconel 718 workpiece. The study primarily highlights the outcome of cryogenically treated soaking duration of tools along with other important process variables, viz. discharge current, open-circuit voltage, pulse-on time, duty factor and flushing pressure, on the performance measures such as electrode wear ratio (EWR), surface roughness and radial over-cut. The study revealed that soaking duration in deep cryo-treatment of the electrode is a significant variable to achieve improved machining characteristics. The performance measures are converted into equivalent single performance measure by calculating the relative closeness coefficient by the techniques for order preferences by similarity to ideal solution (TOPSIS) approach. Finally, a novel teaching–learning-based optimization (TLBO) algorithm has been proposed to find the optimal level of machining variables for the performance measures. The optimal levels of cutting variables obtained through the algorithm are validated through confirmation test, predicting an error of 2.171 percentages between the computational and experimental results. The predicted result suggests that the proposed model can be used to select the ideal process states to achieve productivity for the cryo-treated EDM.

Due to intense competition in the market place, effective scheduling has now become an important issue for the growth and survival of manufacturing firms. To sustain in the current competitive environment, it is essential for the manufacturing firms to improve the schedule based on simultaneous optimization of performance measures such as makespan, flow time, and tardiness. The current paper presents a novel particle swarm optimization (PSO) algorithm for solving multi-objective flexible job shop scheduling problem with the goal of finding approximations of the optimal Pareto front. The Pareto-optimal solutions obtained through multi-objective particle swarm optimization (MOPSO) have been ranked by the composite scores obtained through maximum deviation theory (MDT) to avoid subjectiveness and impreciseness in the decision-making. The results are compared with non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective evolutionary algorithm (MOEA) in terms of four performance metrics. Twenty-eight benchmark instances from literature are solved by the proposed algorithm. It is observed that MOPSO outperforms NSGA-II and MOEA in four performance metrics in most of the instances.

This article considers the problem of scheduling n jobs on m identical parallel processors where an optimal schedule is defined as one that produces minimum makespan (the completion time of the last job) and total tardiness among the set of schedules. Such a problem is known as identical parallel processor makespan and total tardiness problem. In order to minimize makespan and total tardiness of identical parallel processors, improved versions of particle swarm optimization and harmony search algorithm are proposed to enhance scheduling performance with less computational burden. The major drawback of particle swarm optimization in terms of premature convergence at initial stage of iterations is avoided through the use of mutation, a commonly used operator in genetic algorithm, by introducing diversity in the solution. The proposed algorithm is termed as particle swarm optimization with mutation. The convergence rate of harmony search algorithm is improved by fine tuning of parameters such as pitch adjusting rate and bandwidth for improving the solution. The performance of the schedules is evaluated in terms of makespan and total tardiness. The results are analyzed in terms of percentage deviation of the solution from the lower bound on makespan. The results indicate that particle swarm optimization with mutation produces better solutions when compared with genetic algorithm and particle swarm optimization in terms of average percentage deviation. However, harmony search algorithm outperforms genetic algorithm, particle swarm optimization, and particle swarm optimization with mutation in terms of average percentage deviation. In certain instances, the solution obtained by harmony search algorithm outperforms existing clonal selection particle swarm optimization.

This paper proposes an experimental investigation and optimization of the various machining parameters for the electrical discharge machining (EDM) processes on Inconel 718 super alloy using a multi objective particle swarm optimization (MOPSO) algorithm. A Box-Behnkin design of response surface methodology has been used to collect data for the study. The machining performances of the process are evaluated in terms of material removal rate (MRR) and surface quality which are functions of process variables such as open circuit voltage, discharge current, pulse-on-time, duty factor, flushing pressure and tool material. Mathematical model is developed relating responses with process variables. Finally, a MOPSO algorithm has been proposed for the multi objective optimization of the responses.

The study presents experimental investigation to assess influence of cryogenic treatment of electrodes on machinability in powder-mixed electrical discharge machining (PMEDM). Response surface methodology is used to extract maximum process related information with less number of experimental runs. The machining characteristics of the process are evaluated in terms of material removal rate (MRR), electrode wear ratio (EWR) and surface roughness which are function of machining parameters viz. open circuit voltage, discharge current, pulse-on-time, duty factor, concentration of fine graphite powder and cryogenically treated work tool pair. The study reveals that discharge current, pulse-on-time, concentration of powder and work-tool pair exhibit significant effect on all the performance measures. It is also observed that treatment to both the electrodes results in more beneficial in comparison to treatment of either the tool or work piece. Finally, a novel multi-objective particle swarm optimisation algorithm (MOPSO) has been proposed in order to achieve optimal Pareto front.

This paper addresses a robust schedule for a flexible job shop scheduling problem with random machine breakdown. A multi objective framework based on quantum particle swarm optimisation (QPSO) is proposed to generate the predictive schedules that can simultaneously optimise the makespan and the robust measures. The results indicate that the proposed QPSO algorithm is quite effective in reducing makespan in the event that uncertainty is encountered in terms of stochastic machine breakdown. An exhaustive experimental study is conducted to study the effect of different proposed robustness measures on the generated schedules using benchmark problems.

In this work, a particle swarm optimisation (PSO) algorithm with chaotic mutation operator is proposed to solve flexible flow shop scheduling problems. Mutation, a commonly used operator in genetic algorithm, has been introduced so that common problem of trapping of solutions at local minima in PSO can be avoided. Chaotic sequence using logistic mapping is used instead of random numbers to improve the diversity in solution space. The performance of schedules is evaluated in terms of total completion time or makespan (Cmax). The results are presented in terms of percentage deviation (PD) of the solution from the lower bound (LB). The results are compared with different versions of genetic algorithm (GA) used for the purpose from open literature. The results indicate that the proposed PSO algorithm is quite effective in reducing makespan because average percentage deviation is observed as 6.390 whereas GA produces an average percentage deviation of 9.657. Finally, influence of various PSO parameters on solution quality has been investigated.