EXTREQ-2 Software Package for Mathematical Modeling of Multicomponent Extraction Equilibria

Introduction. The Grid Connected Photovoltaic System comprises two fundamental control loops: an external loop responsible for overseeing the DC link voltage, and an internal control loop that regulates the inverter current. The primary element of any control loop is the proportional-integral controller and determining the appropriate gains for this controller is a difficult issue.Aim of the Study. The study aimed to adjust the gains of the PI controllers in both static and dynamic irradiance scenarios for improving DC-link voltage by novel hybrid optimization method named Genetic Algorithm- Simulated Annealing and Genetic Algorithm- Pattern search.Material and Methods. In this paper we use two hybrid optimizations techniques called Genetic Algorithm- simulated Annealing and Genetic Algorithm- Pattern Search to adjust the gains of the PI controllers in both static and dynamic irradiance scenarios for improving DC-link voltage.Results. Finally, this study presents comparison of DC-link voltage with six cases with manual tuning of PI controller, as well as PI controller by Genetic Algorithm- simulated Annealing, Genetic Algorithm- Pattern Search, Genetic Algorithm, Simulated Annealing and Pattern Search. The comparison showed by using Genetic Algorithm-Simulated Annealing, peak overshoot in DC-link voltage is 829.3 V while peak overshoot in DC-link voltage is 1 052 V when DC-link voltage is controlled by manual tuning of PI as well as significant reduction in peak time and settling time in DC-link voltage.Discussion and Conclusion. The results achieved to strengthen the DC-link voltage under both static and dynamic irradiance conditions enable the sustaining of a constant DC-link voltage, which is essential for grid-connected photovoltaic systems. The comparison showed by using Genetic Algorithm- Simulated Annealing, peak overshoot in DC-link voltage is 829.3 V while peak overshoot in DC-link voltage is 1 052 V when DC-link voltage is controlled by manual tuning of PI as well as significant reduction in peak time and settling time in DC-link voltage.

Benchmarking different optimization algorithms is tasky, particularly for network-based cellular communication systems. The design and management process of these systems involves many stochastic variables and complex design parameters that demand an unbiased estimation and analysis. Though several optimization algorithms exist for different parametric modeling and tuning, an in-depth evaluation of their functional performance has not been adequately addressed, especially for cellular communication systems. Firstly, in this paper, nine key numerical and global optimization algorithms, comprising Gauss–Newton (GN), gradient descent (GD), Genetic Algorithm (GA), Levenberg–Marguardt (LM), Quasi-Newton (QN), Trust-Region–Dog-Leg (TR), pattern search (PAS), Simulated Annealing (SA), and particle swam (PS), have been benchmarked against measured data. The experimental data were taken from different radio signal propagation terrains around four eNodeB cells. In order to assist the radio frequency (RF) engineer in selecting the most suitable optimization method for the parametric model tuning, three-fold benchmarking criteria comprising the Accuracy Profile Benchmark (APB), Function Evaluation Benchmark (FEB), and Execution Speed Benchmark (ESB) were employed. The APB and FEB were quantitatively compared against the measured data for fair benchmarking. By leveraging the APB performance criteria, the QN achieved the best results with the preferred values of 98.34, 97.31, 97.44, and 96.65% in locations 1–4. The GD attained the worst performance with the lowest APE values of 98.25, 95.45, 96.10, and 95.70 in the tested locations. In terms of objective function values and their evaluation count, the QN algorithm shows the fewest function counts of 44, 44, 56, and 44, and the lowest objective values of 80.85, 37.77, 54.69, and 41.24, thus attaining the best optimization algorithm results across the study locations. The worst performance was attained by the GD with objective values of 86.45, 39.58, 76.66, and 54.27, respectively. Though the objective values achieved with global optimization methods, PAS, GA, PS, and SA, are relatively small compared to the QN, their function evaluation counts are high. The PAS, GA, PS, and SA recorded 1367, 2550, 3450, and 2818 function evaluation counts, which are relatively high. Overall, the QN algorithm achieves the best optimization, and it can serve as a reference for RF engineers in selecting suitable optimization methods for propagation modeling and parametric tuning.

Potential use of synergist D2EHPA/Cyanex 302 in kerosene system for reactive extraction: Zinc recovery and organic phase regeneration

Optimisation of detailed combustion mechanisms means that the corresponding kinetic model is fitted to experimental data via optimising their important rate and thermodynamic parameters within their domain of uncertainty. Typically, several dozen parameters are fitted to several hundred to several thousand data points. Many numerical optimisation methods have been used, but the efficiency of these methods has not been compared systematically. In this work, parameters of an H2/O2/NO x mechanism (214 reaction steps of 35 species) were fitted to 1552 indirect (ignition delay times measured in shock tubes and concentrations measured in flow reactors) and 755 direct measurements. Three test cases were investigated: (1) fitting the Arrhenius parameters of 2 reaction steps to 732 data points; (2) fitting the Arrhenius parameters of 4 reaction steps to 1077 data points; (3) fitting the Arrhenius parameters of 10 reaction steps to 2307 data points. All 3 cases were investigated in 2 ways: fitting the A-parameters only and fitting all Arrhenius parameters (5, 11 and 29 parameters, respectively). A series of global (FOCTOPUS, genetic algorithm, simulated annealing, particle swarm optimisation, covariance matrix adaptation evolutionary strategy (CMA-ES)) and local (simplex, pattern search, interior-point, quasi-Newton, BOBYQA, NEWUOA) optimisation methods were tested on these cases, some of them in two variants. The methods were compared in terms of the final error function value and number of error function evaluations. The main conclusions are that the FOCTOPUS resulted in the lowest final error value in all cases, but this method required relatively many error function evaluations. As the task became more difficult, more and more methods failed. A variant of the BOBYQA method looked stable and efficient in all cases.

A step-wise genetic algorithm (GA)-based approach is put forward for (a) guiding any interested country on how to optimally improve its environmental sustainability (ES), and (b) determining the characteristics of the (theoretically) maximally sustainable country. The Environmental Sustainability Index (ESI) 2005 is used to this end, with the GA genes, chromosomes, and fitness values corresponding to the ESI 2005 indicators, sets of indicator values, and scores, respectively. Following GA operation, any interested country can increase — and, ideally, maximise — its ES via the following procedure: a chromosome with similar gene values to the ESI indicator values of the interested country, but demonstrating higher fitness (ESI score), is appropriately selected from any GA generation such that the changes that are necessary in order for the country's ESI indicator values to match those of the selected chromosome to be not only implementable, but furthermore capable of producing the maximal improvement in terms of ES. By repeating this procedure on each updated version of the interested country, the country's indicator values and ESI score are progressively modified towards maximal ES. Alternative optimisation approaches, such as pattern search (PS) and simulated annealing (SA), are also capable of reaching maximal ES, yet — due to their single-state basis of operation — do not permit the concurrent investigation of alternative paths towards maximal ES, and — furthermore — do not guarantee the identification of the appropriate indicator(s) to be modified. An interesting finding that is common to GA, PS, and SA operation is that the fitness values of the best chromosomes/states invariably exceed not only the ESI score of the top-ranking ESI 2005 country (75.1 of Finland), but — furthermore — the “perfect” ESI 2005 score of 100 by around 10, thus putting into question the construction details of the ESI 2005.1

Synergistic solvent extraction of lanthanide ions with mixtures of D2EHPA and MIDPA in phosphonium-based ionic liquids

Features of several well-known global and local optimization methods were discussed and their robustness and efficiency were tested on several artificial test functions in Matlab environment. The tested local methods were the interior-point, the quasi-Newton method, Nelder–Mead simplex, the pattern search, the NEWUOA and the BOBYQA methods. The global methods were the genetic algorithm (GA), the simulated annealing (SA), the particle swarm optimization (PSO), and the covariance matrix adaptation evolutionary strategy (CMA-ES) methods (see subsections 2.2 and 2.3 for their details). Furthermore, a novel global optimization method, called FOCusing robusT Optimization with Uncertainty-based Sampling (FOCTOPUS), which proved to be very efficient in the optimization of constrained and highly correlated parameters of combustion kinetic models, was also tested. The test functions were selected in such a way that they had a variety of features: uni-modal and multi-modal, differentiable and non-differentiable, separable and non-separable, low dimensional and high dimensional. The following test functions were used: the 20D Alpine function, the 4D Ackley function, the Cross-in-tray 2D function, the Hartmann-6D function, the Holder table 2D function, the 5D Rastrigin function, the 5D Rosenbrock function, the 4D modified Rosenbrock function, the 20D Zakharov function and a typical 2D multi-modal function. The general conclusion here is that, the global methods performed well on the multi-modal and high-dimensional test functions while the local methods were superior in the case of low-dimensional and uni-modal test functions. For the highly multi-modal test functions, the GA was better than all the other methods. The FOCTOPUS method proved to be inferior to GA for most of the test functions, thus its application cannot be generally recommended.

Velocity picking is an important step for seismic data processing. It is to pick several time-velocity pairs forming a polyline in a semblance image to represent the time and velocity relation in layers. Conventionally the geophysicists did it, but it took much time. We transfer it to a combinatorial problem which is finding the best combination from the set of candidate points. We define an objective function of energy that includes total semblance value of picked points, and constraints on the number of picked points, interval velocity, and velocity slope. We adopt three optimization methods: simulated annealing (SA), genetic algorithm (GA), and Hopfield neural network (HNN), to find the optimal solution of objective function and obtain the best polyline consisting of picked peak points respectively. In SA, the random system state represents a solution, and the higher energy random system state has a certain probability to be accepted and skip the local minimum. After annealing, the lowest energy system state is the best polyline. It is a global optimal solution. In GA, an individual represents a solution. Several individuals evolve many generations. The highest fitness individual in the last generation is the best polyline. It is also a global optimal solution. In HNN, the neurons of network represent a solution. We derive the equation of motion from the objective function and use asynchronous updating to renew the neurons of network. Finally, the network converges. The stable network state represents the best polyline. In GA, we find the maximum of the objective function. In SA and HNN, we find the minimum of the objective function. In the implementations of SA and HNN, we change the objective function to a negative function and find the minimum. In the parameter settings of SA and GA, we find the best parameter settings by sequential method. We have experiments on simulation data and Nankai real seismic data. We have 22 common midpoint gathers (CMP gathers) of simulated seismic data and 15 CMP gathers of Nankai real seismic data for experiments. We evaluate the performance by comparing the mean difference between the picking result of each adopted method and that of human. The experiments show that GA has the best result on the simulated and real seismic data experiments. The best picking results by three methods are further used to do normal move-out (NMO) correction and stacking. The results show that both of the signals of the simulated and real seismic data are enhanced. The results of velocity picking by three optimization methods will be helpful for further seismic data processing and interpretation.

Hybrid membrane-distillation systems are reported to be an energy-efficient separation processes compared to conventional distillation process; especially for separating close-boiling mixtures. However, several optimization methods are available and can be applied to find a cost-effective design and operation of hybrid membrane-distillation systems. This work evaluates the hybrid system design in terms of energy and compares the performance (i.e. the accuracy and computational efficiency) of stochastic optimisation method (Simulated Annealing) and deterministic method (Pattern search method) for optimisation of process operating conditions. The parallel hybrid membrane-distillation system for the separation of ethylene-ethane mixture is selected for analysis. The decision variables manipulated for the optimisation are the stage cut and permeate pressure. Results show that pattern search is faster than Simulated Annealing but that is more able to find the close-global results. The difference between the optimal result found by Simulated Annealing and pattern search is about 0.02 % for the total operating costs, 0.3 % for the stage cut and 5 % for the permeate pressure. These findings suggest that it is possible to use deterministic method for optimisation complex system. However, to locate an optimal solution and increase confidence in the result, a variety of initial points should be tested.

Comparison and improvements of optimization methods for gas emission source identification

The IC lead frame is an essential component in semiconductor packaging, primarily composed of a nickel (Ni)–copper (Cu) alloy in which Ni is electroplated onto a Cu substrate. In this study, synergistic solvent extraction using a binary extractant containing LIX984N and Cyanex302, followed by two-stage selective stripping using sulfuric acid (H2SO4) and nitric acid (HNO3) as the stripping agent, was employed to separate and recover Cu and Ni from the leachate of an IC lead frame. The results indicated that under the optimal conditions of synergistic solvent extraction with an extraction pH value of 1, an extractant concentration of 0.015 M LIX984N + 0.0375 M Cyanex302, an extraction aqueous/organic (A/O) ratio of 1:1, and an extraction time of 5 min, the extraction efficiencies for Cu and Ni were 99.8% and 1.17%, respectively. The distribution ratios were DCu 999 and DNi 0.012, resulting in a separation factor of 83,250. In addition, the separation factor was much higher than that of using individual extractant of LIX984N (6208.3) or Cyanex302 (22,185.2). Subsequently, under optimal first-stage stripping conditions, using 0.05 M H2SO4 at a stripping organic/aqueous (O/A) ratio of 1:1, and with a stripping time of 3 min, a stripping efficiency of 99.9% for Ni was achieved. Next, under optimal second-stage stripping conditions, using 5 M HNO3 at a stripping O/A ratio of 2:1, and with a striping time of 3 min, a stripping efficiency of 99.9% for Cu was achieved. Finally, sodium hydroxide (NaOH) was added to the respective stripping solutions to precipitate Ni and Cu ions, followed by calcination treatment for the precipitates to obtain NiO and CuO, respectively. The purity of the former was 99.74% and that of the latter was 99.82%. The results demonstrate that synergistic solvent extraction using a binary extractant containing LIX984N and Cyanex302 can almost entirely extract Cu in the leachate of an IC lead frame at a lower extraction pH and a lower extractant concentration, thus reducing the co-extraction of Ni. In addition, less co-extracted Ni in the organic phase can be selectively stripped using dilute H2SO4, thus reducing the co-stripping of Cu. Hence, the effective separation and recovery of Cu and Ni in IC lead frame leachate can be achieved, which contributes to improving the sustainability of natural resources.

Novel computing paradigms for parameter estimation in Hammerstein controlled auto regressive auto regressive moving average systems

This paper analyzes the functional feasibility and strategic value of hybrid hydrogen storage and photovoltaic (PV) energy systems at isolated areas, specifically at Egypt’s Shalateen station. The paper is significant as it formulates a solution to the energy independence coupled with economic feasibility issue in regions where the basic energy infrastructure is non-existent or limited. Through the integration of a portfolio of advanced optimization algorithms—Differential Evolution (DE), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Multi-Objective Genetic Algorithm (MOGA), Pattern Search, Sequential Quadratic Programming (SQP), and Simulated Annealing—the paper evaluates the performance of two scenarios. The first evaluates the PV system in the absence of hydrogen production to demonstrate how system parameters are optimized by Pattern Search and PSO to achieve a minimum Cost of Energy (COE) of 0.544 USD/kWh. The second extends the system to include hydrogen production, which becomes important to ensure energy continuity during solar irradiation-free months like those during winter months. In this scenario, the same methods of optimization enhance the COE to 0.317 USD/kWh, signifying the economic value of integrating hydrogen storage. The findings underscore the central role played by hybrid renewable energy systems in ensuring high resilience and sustainability of supplies in far-flung districts, where continued enhancement by means of optimization is needed to realize maximum environmental and technological gains. The paper offers a futuristic model towards sustainable, dependable energy solutions key to the energy independence of the future in such challenging environments.

MATLAB® builds in a number of derivative-free optimisers (DFOs), conveniently providing tools beyond conventional optimisation means. However, with the increase of available DFOs and being compounded by the fact that DFOs are often problem dependent and parameter sensitive, it has become challenging to determine which one would be most suited to the application at hand, but there exist no comparisons on MATLAB DFOs so far. In order to help engineers use MATLAB for their applications without needing to learn DFOs in detail, this paper evaluates the performance of all seven DFOs in MATLAB and sets out an amalgamated benchmark of multiple benchmarks. The DFOs include four heuristic algorithms - simulated annealing, particle swarm optimization (PSO), the genetic algorithm (GA), and the genetic algorithm with elitism (GAe), and three direct-search algorithms - Nelder-Mead’s simplex search, pattern search (PS) and Powell’s conjugate search. The five benchmarks presented in this paper exceed those that have been reported in the literature. Four benchmark problems widely adopted in assessing evolutionary algorithms are employed. Under MATLAB’s default settings, it is found that the numerical optimisers Powell is the aggregative best on the unimodal Quadratic Problem, PSO on the lower dimensional Scaffer Problem, PS on the lower dimensional Composition Problem, while the extra-numerical genotype GAe is the best on the Varying Landscape Problem and on the other two higher dimensional problems. Overall, the GAe offers the highest performance, followed by PSO and Powell. The amalgamated benchmark quantifies the advantage and robustness of heuristic and population-based optimisers (GAe and PSO), especially on multimodal problems.

Numerical treatment for boundary value problems of Pantograph functional differential equation using computational intelligence algorithms