Optimization of the performance of flotation circuits using a genetic algorithm oriented by process-based rules

PurposeThe purpose of this paper is to deal with the performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm. It provides the optimum unit availability level for different combinations of failure and repair rates of the subsystems of the stock preparation unit of the paper plant concerned.Design/methodology/approachIn this paper, efforts have been made to develop performance models based on real situations for the stock preparation unit. The performance in terms of availability has been evaluated on the basis of Markov birth‐death process. After that, the performance optimization using genetic algorithm is done, which gives the optimum unit availability levels for different combinations of failure and repair rates of the subsystems of stock preparation units for enhancing the overall performance of the paper plant.FindingsThe effect of genetic algorithm parameters, namely number of generations, population size and crossover probability on the unit performance i.e. availability has been analyzed and discussed with the concerned paper plant management. It is found that these results are highly beneficial to the maintenance engineers for the purpose of effective maintenance planning to enhance the overall performance (availability) of the stock preparation unit of the paper plant.Originality/valueMost of the researchers have confined their work to the development and analysis of theoretical models which has little practical significance. To fulfill this deficiency, efforts have been made in the present work to develop a model based on real situations for the stock preparation unit.

The integration of discrete simulations, artificial intelligence methods, and the theory of probability in order to obtain a high flexibility of the production system is crucial. In this paper, the concept of a smart factory operation is proposed along with the idea of data exchange architecture, simulation creation, performance optimization, and predictive analysis of the production process conditions. A Digital Twin for a hybrid flow shop from the automotive industry is presented as a case study. In the paper, the Ant Colony Optimization (ACO) algorithm is developed for multi-criteria scheduling problems in order to obtain a production plan without delays and maximum resource utilization. The ACO is compared to the immune algorithm and genetic algorithm. The best schedules are achieved with low computation time for the Digital Twin. By predicting the reliability parameters of the limited resources of the Digital Twin, stable deadlines for the implementation of production tasks are achieved. Mean Time To Failure and Mean Time of Repair are predicted for a real case study of an electric steering gear production line. The presented integration and data exchange between the elements of the smart factory: a Digital Twin, a computing module including an optimization, prediction, and simulation methods fills the gap between theory and practice for Industry 4.0. The paper presents measurable benefits of integration of discrete simulation tools, historical data analysis, and optimization methods.

This paper describes a symmetrical, simple pair circuit that can harness thermal noise energy to operate correctly on a “single-dopant device” and its application to a full adder circuit. The single-dopant device is an atomic scale solid-state device and can be used, for example, as an actual platform for a single-electron circuit. We here aim to fabricate actual single-dopant circuits with a deterministic doping method. As a first step toward this, we design a possible circuit on the device and test its operation by Monte Carlo computer simulations. The simulation results confirmed correct circuit operation and showed that the device exhibited thermal-noise-harnessing abilities. Moreover, we also found that the simple circuit can operate as a full adder circuit. We believe that we will successfully fabricate a simple, actual single-dopant full-adder circuit in the near future.

In order to optimize the kinematic and dynamic performance of a 2-DoF planar parallel mechanism used in hybrid machines, a multi-objective optimization method is introduced. Based on the compare of the common kinematic analysis method and dynamic analysis method, the kinematic and dynamic performances evaluation indices which can be used in the performance optimization are introduced. Then a multi indices optimization method based on genetic algorithm is investigated to achieve the multi performance optimization of the parallel mechanism. In the optimization method, the kinematic, stiffness and dynamic performances are improved by optimizing the geometric and structural parameters of the parallel mechanism. The simulation result indicates that the optimization method is of practical value to improve the development of the parallel mechanism.

A new performance and area optimization algorithm for complex VLSI systems is presented. It is widely believed within the VLSI CAD community that the relationship between delay and silicon area of a VLSI chip is convex. This conclusion is based on a simplified linear RC model to predict gate delays. In the proposed optimization algorithm, a nonlinear, non‐RC based transistor delay model was used which resulted in a non‐convex relationship between the delay and the silicon area of a VLSI chip. Genetic algorithms are better suited for discrete, non‐convex, non‐linear optimization problems than traditional calculus‐based algorithms. By using the genetic algorithms in the performance and area optimization, we are able to find the optimal values for both delay and silicon area for the ISCAS benchmark circuits.

Extensive ab initio Gaussian-3-type calculations of potential energy surfaces (PES), which are expected to be accurate within 1-2 kcal/mol, combined with statistical theory calculations of reaction rate constants have been applied to study various possible pathways in the hydrogen abstraction acetylene addition (HACA) mechanism of naphthalene and acenaphthalene formation as well as Diels-Alder pathways to acenaphthalene, phenanthrene, and pyrene. The barrier heights; reaction energies; and molecular parameters of the reactants, products, intermediates, and transition states have been generated for all types of reactions involved in the HACA and Diels-Alder mechanisms, including H abstraction from various aromatic intermediates, acetylene addition to radical sites, ring closures leading to the formation of additional aromatic rings, elimination of hydrogen atoms, H disproportionation, C2H2 cycloaddition, and H2 loss. The reactions participating in various HACA sequences (e.g., Frenklach's, alternative Frenklach's, and Bittner and Howard's routes) are demonstrated to have relatively low barriers and high rate constants under combustion conditions. A comparison of the significance of different HACA mechanisms in PAH growth can be made in the future using PES and molecular parameters obtained in the present work. The results show that the Diels-Alder mechanism cannot compete with the HACA pathways even at high combustion temperatures, because of high barriers and consequently low reaction rate constants. The calculated energetic parameters and rate constants have been compared with experimental and theoretical data available in the literature.

A new optimization method: Electro-Search algorithm

The advancement of fiber-reinforced composites has revolutionized the design and development of high- performance materials for aerospace, automotive, and structural applications. However, the complexity of composite systems—arising from heterogeneous microstructures, multiple reinforcement mechanisms, and nonlinear mechanical behavior—poses challenges in predictive modeling and performance optimization. Recent developments in artificial intelligence (AI) and computational intelligence (CI) have enabled efficient modeling, simulation, and optimization of these materials by integrating machine learning, deep learning, and evolutionary algorithms. This study reviews and analyzes AI-driven methods applied to fiber-reinforced metal matrix composites (MMCs) and polymer matrix composites (PMCs), emphasizing data-driven prediction of mechanical, thermal, and tribological properties. The paper further explores hybrid computational models combining finite element analysis (FEA) with neural networks, genetic algorithms (GA), and fuzzy logic to achieve enhanced predictive accuracy and process parameter optimization. The proposed framework demonstrates the potential of AI–CI methods in accelerating material design cycles and optimizing composite fabrication processes with improved precision, reduced experimental cost, and higher reliability.

Influence of waterfall aeration and seasonal temperature variation on the iron and arsenic attenuation rates in an acid mine drainage system

It is important to design and operate energy conversion systems such as gas turbine cogeneration ones optimally from the thermoeconomic viewpoint. However, an energy conversion system has a complex network structure, and it takes much time to create its model for the thermoeconomic analysis and optimization. In this paper, a systems approach is presented for the performance analysis and optimization of mechanical systems with network structures, and it is applied to the thermoeconomic analysis and optimization of a gas turbine cogeneration unit. The system modeling for the performance analysis is conducted by a building block approach. Static and dynamic problems for the performance analysis are formulated as sets of nonlinear algebraic and differential algebraic equations, and are solved by the Newton-Raphson method and a hierarchical combination of the Runge-Kutta and Newton-Raphson methods, respectively. The performance optimization is conducted to determine design and operation conditions which optimize performance criteria. This problem is formulated as a nonlinear programming one and is solved by a global optimization method. In the application, the cycle analysis is conducted to determine mass flow rates, pressures, and temperatures, which is followed by the exergy and cost analyses to determine exergy flow rates and efficiencies, and capital costs, respectively. In addition, design and operation conditions are determined to maximize the exergy efficiency or minimize the annual total cost based on the results of the cycle, exergy, and cost analyses. Through a numerical study, it turns out that the proposed systems approach enables one to conduct the thermoeconomic analysis and optimization efficiently.

Genetic algorithms for large-scale optimization in chemometrics: An application

This research work aims at evaluating the possibilities of using artificial intelligence in dynamic resource management and optimization of software system performance. In today’s complex world of application usage, normal methods of resource management are unable to cater to these dynamic needs and fulfill its usage potential. In this work, an assessment of three mainstream AI techniques – reinforcement learning, neural network, and genetic algorithm – is performed based on performance indicators such as resource utilization and consumption, average response time, throughput, costs, prediction capability, stability, and time taken to converge. The results show that the neural networks have the best resource acquisition performance as well as response rates, while the reinforcement learning has the best cost management and flexibility rates. As it has been pointed out, genetic algorithms are quite useful in finding optimization solutions, however real-time responsiveness is lack. Thus, the results provide significant understating of how to choose the proper AI technique depending on the specific application needs which in turn will be useful for organizations willing to improve their resource management using AI-based solutions.

Laptop personal computers (LPCs) and their components are vulnerable devices in harsh mechanical environments. One of the most sensitive components of LPCs is hard disk drive (HDD) which needs to be protected against damages attributable to shock and vibration in order to have better magnetic read/write performance. In the present work, a LPC and its HDD are modeled as two degrees of freedom system and the nonlinear optimization method is employed to perform a passive control through minimizing peak of HDD absolute acceleration caused by a base shock excitation. The presented shock excitation is considered as half-sine pulse of acceleration. In addition, eleven inequality constraints are defined based on geometrical limitations and allowable intervals of lumped modal parameters. The target of the optimization is to reach optimum modal parameters of rubber mounts and rubber feet as design variables and subsequently propose new characteristics of rubber mounts and rubber feet to be manufactured for the HDD protection against shock excitation. The genetic algorithm and the modified constrained steepest descent algorithm are employed in order to solve the nonlinear optimization problem for three widely-used commercial cases of HDD. Finally, the results of both optimization methods are compared to make sure about their accuracy.

Genetic algorithms (GA) are stochastic search procedures which have been used for solving many complex optimization problems. It is obvious that GA collect and exploit some statistical information about the search space, but this information isn't processed in explicit way. In this paper, we consider GA with binary representation and its explicit statistics in a form of probability distribution of unit-values. The binary GA convergence property is discussed and a new convergence prediction method is proposed. The results of the prediction algorithm effectiveness investigation over the set of complex continuous and discrete deceptive problems are presented.

Genetic algorithm (GA) is an effective method to tackle combinatorial optimization problems. Since the limitation of encoding method, the search space of GA should be regular. Unfortunately, for constraint optimizations, this precondition is unsatisfied. To obtain a regular search space, a commonly used method is penalty functions. But the setting of a good penalty function is difficult. In this paper, a novel algorithm, called search space filling and shrinking algorithm (SSFSA), is proposed. SSFSA first seeks a smaller search space which covers all the feasible domains, then fills the unfeasible search space to acquire a regular search space. Search space shrinking diminishes the search space, so shortens the searching time. Search space filling repairs the irregular search space, and makes GA execute effectively. Experimental results show that SSFSA outperforms penalty methods'.