Improved Optimization Technique Research Articles

Optimization and predictive modeling of membrane based produced water treatment using machine learning models

Membrane technology has become a crucial solution for treating produced water, playing a vital role in resource recovery and environmental preservation. The use of advanced Artificial Intelligence (AI) technique such as Machine Learning (ML) has introduced an innovative approach to define and model these treatment procedures such as trans-membrane pressure (TMP) and total membrane resistance. The research focuses specifically on the utilization of Artificial Neural Network (ANN) and Extreme Gradient Boosting (XGBoost) model to improve the precision and effectiveness of produced water treatment using membrane technology. Also, the study presented explores how the Artificial Neural Network (ANN) framework excels in predictive insights and process modeling, optimizing operational parameters in membrane-based treatment setups. Furthermore, integrating the Extreme Gradient Boosting (XGBoost) model highlights its ability to enhance system performance, providing accurate predictions and better control in complex treatment processes. It presents comprehensive research of combining Artificial Neural Network (ANN) and Extreme Gradient Boosting (XGBoost) model with membrane technology for produced water treatment, emphasizing their potential to improve optimization techniques and modeling approaches, ultimately enhancing water treatment efficiency and sustainability. Both the Artificial Neural Network (ANN) and Extreme Gradient Boosting (XGBoost) models achieved an impressive R-squared (R2) scores of 0.754, and 0.97 for Transmembrane Pressure (TMP), and 0.95 and 0.81 for Total Resistance prediction respectively.

Machinability of different cutting tool materials for electric discharge machining: A review and future prospects

Electric Discharge Machining (EDM) is essential for shaping and cutting tool steel. EDM’s precision in machining difficult materials and tool steel characteristics are well known. EDM efficiency requires reliable performance measurement parameters. The physical shape and mobility of the electrode tool are critical in EDM research. Layer machining is an advanced method that removes material in a sequential manner to produce intricate 3D shapes in tool steel and several other materials. The improvement in layer machining methods with precise toolpath algorithms, adaptive layer thickness management, and real-time monitoring systems is required to maximize precision and efficiency. Response surface methodology, the artificial neural network, and other techniques are necessary to optimize EDM operations and maximize performance. Many researchers experimented with electrode shapes and movement patterns to enhance the removal of material and the quality of surfaces. Investigation of complex electrode structures and innovative tool path strategies has been performed in previous studies. It was very difficult to consider various factors during the EDM operation; hence, the present review summarizes the positive outcomes of previous research. The review emphasizes optimizing pulse duration and discharge current to improve EDM efficiency. The present comprehensive review discusses research on EDM in three main areas: electrode tool geometry and motion, tool steel layer processing, and factors for measuring EDM performance. The objective of the present review is to focus on measuring material removal rates, surface roughness, tool wear, and energy usage. The present review concludes that EDM is crucial to machining tool steel and cutting tool materials. Integrating and hybrid machining technologies can improve performance, and improved optimization techniques are crucial. It also recognizes knowledge gaps and explores new frontiers in this dynamic field.

An adaptive protection coordination for microgrids utilizing an improved optimization technique for user-defined DOCRs characteristics with different groups of settings considering N-1 contingency

Due to the increased integration of distributed generation (DG) in electric power systems, optimal coordination of overcurrent relays has become a key problem in power distribution systems. However, there is a lack of expertise in the creation of optimum microgrid coordination that takes into account all N-1 scenarios through the nonstandard relay characteristics. This work presents a novel technique for optimal coordination of directional overcurrent relays (DOCRs) in terms of relay curve settings (A and B), time dial setting (TDS) and plug setting (PS) to achieve the shortest running time and attain optimal settings. The optimization is carried out using a modified version of the Hanger Games Search algorithm (MHGS). The performance of the proposed method is assessed using the 14 bus distribution system while considering all the N-1 contingencies. DIgSILENT software was utilized to perform the required power system analysis, such as power flow and short circuit analysis. The MHGS method is used to determine the best settings for the DOCRs problem. The results are compared to the traditional HGS as well as those obtained by other current optimization approaches provided in the literature in order to demonstrate the effectiveness and superiority of the proposed MHGS in lowering relay operation time for optimum DOCRs coordination.

Prediction of Sea Level in the Arabian Gulf Using Artificial Neural Networks

Creating an efficient model for predicting sea level fluctuations is essential for climate change research. This study examined the effectiveness of utilizing Artificial Neural Networks (ANNs), particularly the recurrent network approach. ANNs were chosen for their capacity to learn from extensive and intricate data and their ability to handle nonlinear correlations. The Long Short-Term Memory (LSTM) algorithm was employed to fill data gaps and predict future sea level records in the Arabian Gulf, especially in Mina Salman. The results were promising, with LSTM successfully filling a 6-year data gap while maintaining low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. The first phase of the model yielded a RMSE value of 63.4 mm and a MAPE value of 3.14%. The same approach was used to retrain the model with a mix of real and predicted values, preserving historical patterns and yearly rates with an RMSE of 66.5 mm and a MAPE of 3.07%. These findings highlight LSTM’s advantages when considering only historical information for predicting the future sea level changes. The research provides valuable insights into predicting sea level changes in regions with limited field data, such as the Arabian Gulf, and emphasizes the potential for further research to enhance sea level prediction models through improved optimization techniques.

Chaser Priori Wolf (CPW) Optimization an Improved Optimization Technique Video Content Classification and Detection

Optimizers play a crucial role in video object detection by promoting the training and improving the performance of the model. Optimizers are responsible for minimizing the loss function during training. The parameters of models are updated iteratively based on the gradients of the loss parameters. By continuously adjusting the parameters in the direction of the steepest descent, optimizers guide the model towards convergence, reducing the loss and improving the object detection performance. In the proposed paper hybrid optimizer named chaser priori wolf optimizer is proposed. The chaser priori wolf optimization is based on the hybridization of cat swarm optimization and coyote optimization. Well-known optimizers like SGD, ADAM, adagrad, adadelta and RMSprop are used as default optimizers by researchers. The proposed work introduced CPW optimizer which works for classification to improve the convergence and feature selection. The comparative result showed an increase in the performance of CNN based YOLO model. The results are compared concerning sensitivity, specificity and accuracy. Results clearly showed improvement in all performance metrics and the average improvement in comparison with state of art architecture is 10.3%.

Enhancing frequency stability in diverse power systems with conventional and renewable energy sources based on an innovative LFC and controlled energy storage integration

The rapid advancement of renewable energy sources (RESs) has led to a decrease in system inertia and an exacerbation of the instability issue. Hence, this study puts forward an approach to bolster the stability of the power grid amidst the presence of RESs and disturbances in load conditions. The strategy utilizes controlled energy storage systems, including plug-in electric vehicles and fuel cell systems, along with a secondary controller (SC). To improve the strategy's performance, novel controllers and an enhanced optimization technique are employed. The proposed controller combines proportional-integral-derivative (PID) and fractional order control methods, resulting in a new arrangement. The parameters of the controller are tuned using an improved optimization technique called an Enhanced Runge Kutta Optimizer. The effectiveness of the proposed algorithm is demonstrated through a performance comparison with conventional optimizers, using well-established mathematical equations. Additionally, the proposed controller's effectiveness is verified by comparing its performance with other controller arrangements, such as PID, combining TD-TI, and cascaded (1 + PD)-PID controllers. The performance of the system is enhanced by 66 % more with the proposed controller in SC compared to the (1 + PD)-PID controller, which is regarded as the most optimal among the controllers examined in this study. Additionally, the system's performance is improved by 44 % when the proposed strategy is implemented, as compared to the system's performance without the proposed strategy. Totally, the effectiveness of the proposed strategy is validated across various scenarios, including high penetration of RESs, significant load fluctuations, and system non-linearity.

A Review of Machine Learning Models for Harmful Algal Bloom Monitoring in Freshwater Systems

Highlights Machine Learning (ML) models are identified, reviewed, and analyzed for HAB predictions. Data preprocessing is vital for efficient ML model development. ML models for toxin production and monitoring are limited. Abstract. Harmful algal blooms (HABs) are detrimental to livestock, humans, pets, the environment, and the global economy, which calls for a robust approach to their management. While process-based models can inform practitioners about HAB enabling conditions, they have inherent limitations in accurately predicting harmful algal blooms. To address these limitations, Machine Learning (ML) models can potentially leverage large volumes of IoT data to aid in near real-time predictions. ML models have evolved as efficient tools for understanding patterns and relationships between water quality parameters and HAB expansion. This review describes ML models currently used for predicting and forecasting HABs in freshwater ecosystems and presents model structures and their application for predicting algal parameters and related toxins. The review revealed that regression trees, random forest, Artificial Neural Network (ANN), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) are the most frequently used models for HABs monitoring. This review shows ML models' prowess in identifying significant variables influencing algal growth, HAB drivers, and multistep HAB prediction. Hybrid models also improve the prediction of algal-related parameters through improved optimization techniques and variable selection algorithms. While ML models often focus on algal biomass prediction, few studies apply ML models for toxin monitoring and prediction. This limitation can be associated with a lack of high-frequency toxin datasets for model development, and exploring this domain is encouraged. This review serves as a guide for policymakers and researchers to implement ML models for HAB prediction and reveals the potential of ML models for decision support and early prediction for HAB management. Keywords: Cyanobacteria, Freshwater, Harmful algal blooms, Machine learning, Water quality.

Salp swarm algorithm to solve Short-Term hydrothermal scheduling problem

Abstract Hydrothermal scheduling possibility is regarding the possible performance of the power system was suggested in this article. Hydrothermal power production largely relies on constraints, which are unpredictable in reality. To catch the difficulty associated with hydrothermal power to get optimize power production and then to manage the optimization section, a recently improved optimization technique named Salp Swarm Algorithm has been employed. It essentially operates on the style of Salps. A procedure has been tested for supporting the capacity for solving challenges and for checking the outcome of the hydrothermal power system. To approach the problem very practical the valve-point charging outcome has been regarded also.

An Improved Optimization Technique for Energy Harvesting System with Grid connected Power for Green House Management

An Improved Optimization Technique for Energy Harvesting System with Grid connected Power for Green House Management

Optimal Design of TD-TI Controller for LFC Considering Renewables Penetration by an Improved Chaos Game Optimizer

This study presents an innovative strategy for load frequency control (LFC) using a combination structure of tilt-derivative and tilt-integral gains to form a TD-TI controller. Furthermore, a new improved optimization technique, namely the quantum chaos game optimizer (QCGO) is applied to tune the gains of the proposed combination TD-TI controller in two-area interconnected hybrid power systems, while the effectiveness of the proposed QCGO is validated via a comparison of its performance with the traditional CGO and other optimizers when considering 23 bench functions. Correspondingly, the effectiveness of the proposed controller is validated by comparing its performance with other controllers, such as the proportional-integral-derivative (PID) controller based on different optimizers, the tilt-integral-derivative (TID) controller based on a CGO algorithm, and the TID controller based on a QCGO algorithm, where the effectiveness of the proposed TD-TI controller based on the QCGO algorithm is ensured using different load patterns (i.e., step load perturbation (SLP), series SLP, and random load variation (RLV)). Furthermore, the challenges of renewable energy penetration and communication time delay are considered to test the robustness of the proposed controller in achieving more system stability. In addition, the integration of electric vehicles as dispersed energy storage units in both areas has been considered to test their effectiveness in achieving power grid stability. The simulation results elucidate that the proposed TD-TI controller based on the QCGO controller can achieve more system stability under the different aforementioned challenges.

Power quality improvement of distribution systems asymmetry caused by power disturbances based on particle swarm optimization-artificial neural network

With an increase of non-linear load in today’s electrical power systems, the rate of power quality drops and the voltage source and frequency deteriorate if not properly compensated with an appropriate device. Filters are most common techniques that employed to overcome this problem and improving power quality. In this paper an improved optimization technique of filter applies to the power system is based on a particle swarm optimization with using artificial neural network technique applied to the unified power flow quality conditioner (PSO-ANN UPQC). Design particle swarm optimization and artificial neural network together result in a very high performance of flexible AC transmission lines (FACTs) controller and it implements to the system to compensate all types of power quality disturbances. This technique is very powerful for minimization of total harmonic distortion of source voltages and currents as a limit permitted by IEEE-519. The work creates a power system model in MATLAB/Simulink program to investigate our proposed optimization technique for improving control circuit of filters. The work also has measured all power quality disturbances of the electrical arc furnace of steel factory and suggests this technique of filter to improve the power quality.

EV-Based reconfigurable smart grid management using support vector regression learning technique machine learning

Renewable microgrid (RMG) is a new solution to improve the safety, reliability, quality, and performance of energy in electrical systems. By implementing different renewable energy sources, like photovoltaic panels and wind turbines, RMGs can reduce greenhouse gas emissions and enhance proficiency. The paper suggests a RMG energy management method based on machine learning, which considers a reconfigurable frame according to the remote link switching and segmentation. The proposed scheme investigates advanced support vector machines to model and estimates the charging demand (ChD) of hybrid electric vehicles (HEVs). To reduce the load impact of HEV on the network, 2 various cases have been implemented; one is coordinated, and the other is smartly charged. Because of the complex construction of the problem statement, an improved optimization technique according to the gray wolf is proposed. The simulation outcomes of the IEEE microgrid (MG) trial system depict that it is of adequate and efficient quality in both cases. Based on the result of the forecast for the total charge demand of HEVs, the average absolute percentage error is 0.978 that is so small. Furthermore, the outcomes are shown that, compared to the coordination scheme, the total operating cost of the MG in smart load is reduced by 2.5%.

A W‐band sparse phased array antenna with ultrahigh sparsity for radar applications

AbstractA sparse phased array with 4% sparsity is developed for advanced low‐cost W‐band radar applications. A high‐order radiation mode‐based substrate‐integrated waveguide antenna featuring waveguide feeding is employed as the antenna element to achieve feeding loss reduction and antenna gain enhancement. An improved optimization technique is adopted to optimize the antenna element distribution. The combination of the element distribution and the antenna element radiation pattern achieves grating lobe suppression. Both the simulation and measurement results show that a ±15° beam scanning range can be achieved, and the level of the measured normal and scanning sidelobes is lower than −18 dB and −13 dB, respectively.

Evolving fuzzy k-nearest neighbors using an enhanced sine cosine algorithm: Case study of lupus nephritis

Because of its simplicity and effectiveness, fuzzy K-nearest neighbors (FKNN) is widely used in literature. The parameters have an essential impact on the performance of FKNN. Hence, the parameters need to be attuned to suit different problems. Also, choosing more representative features can enhance the performance of FKNN. This research proposes an improved optimization technique based on the sine cosine algorithm (LSCA), which introduces a linear population size reduction mechanism for enhancing the original algorithm's performance. Moreover, we developed an FKNN model based on the LSCA, it simultaneously performs feature selection and parameter optimization. Firstly, the search performance of LSCA is verified on the IEEE CEC2017 benchmark test function compared to the classical and improved algorithms. Secondly, the validity of the LSCA-FKNN model is verified on three medical datasets. Finally, we used the proposed LSCA-FKNN to predict lupus nephritis classes, and the model showed competitive results. The paper will be supported by an online web service for any question at https://aliasgharheidari.com.

AN IMPROVED OPTIMIZATION TECHNIQUE FOR NETWORK CONTROLLED pH PROCESS AND DC MOTOR USING VARIOUS CONTROLLERS

ABSTRACT Networking has become the paradigm in many of the control industries, to control and diagnose the parameters or the machines. The networked environment is connection of different machines/components connected through a standard medium to transfer and receive data to monitor or control based on the application required in the environment. Generally the processes in industries are classified as the linear process and non-linear process where the classification is based on the information fetched. Linear process are generally controllable process where the errors or the problems occurred are known, whereas in the non-linear process generate random errors which are uncontrollable. In this paper two different systems such as pH process and DC motor is considered for networked control, where pH is a non-linear system and DC motor is a linear system. Majority of the industries work using the water as their main source for the manufacturing. Waste water treatment is a major issue where the quality of the waste water is connected with the pH value measured in the water. Most of the industries use DC motor to run their machines for different applications. Control of DC motor for multiple applications is considered as a difficult process where all the motors are to run at the same speed. Therefore to control both pH and DC motor Networked Control System is proposed to connect the process with the controllers. The proposed system has been concluded that Fuzzy Logic Controller under the networked environment has shown better results where the settling time and overshoot of pH and DC motor has been is 60 seconds & 0% and 8 seconds & 5.3% respectively, compared with the other controllers.

Expensive multi-objective optimization of electromagnetic mixing in a liquid metal

This paper presents a novel trust-region method for the optimization of multiple expensive functions. We apply this method to a biobjective optimization problem in fluid mechanics, the optimal mixing of particles in a flow in a closed container. The three-dimensional time-dependent flows are driven by Lorentz forces that are generated by an oscillating permanent magnet located underneath the rectangular vessel. The rectangular magnet provides a spatially non-uniform magnetic field that is known analytically. The magnet oscillation creates a steady mean flow (steady streaming) similar to those observed from oscillating rigid bodies. In the optimization problem, randomly distributed mass-less particles are advected by the flow to achieve a homogeneous distribution (objective function 1) while keeping the work done to move the permanent magnet minimal (objective function 2). A single evaluation of these two objective functions may take more than two hours. For that reason, to save computational time, the proposed method uses interpolation models on trust-regions for finding descent directions. We show that, even for our significantly simplified model problem, the mixing patterns vary significantly with the control parameters, which justifies the use of improved optimization techniques and their further development.

An improved optimization technique using Deep Neural Networks for digit recognition

In the world of information retrieval, recognizing hand-written digits stands as an interesting application of machine learning (deep learning). Though this is already a matured field, a way to recognize digits using an effective optimization using soft computing technique is a challenging task. Training such a system with larger data often fails due to higher computation and storage. In this paper, a recurrent deep neural network with hybrid mini-batch and stochastic Hessian-free optimization (MBSHF) is for accurate and faster convergence of predictions as outputs. A second-order approximation is used for achieving better performance for solving quadratic equations which greatly depends on computation and storage. Also, the proposed technique uses an iterative minimization algorithm for faster convergence using a random initialization though huge additional parameters are involved. As a solution, a convex approximation of MBSHF optimization is formulated and its performance on experimenting with the standard MNIST dataset is discussed. A recurrent deep neural network till a depth of 20 layers is successfully trained using the proposed MBSHF optimization, resulting in a better quality performance in computation and storage. The results are compared with other standard optimization techniques like mini-batch stochastic gradient descent (MBSGD), stochastic gradient descent (SGD), stochastic Hessian-free optimization (SHF), Hessian-free optimization (HF), nonlinear conjugate gradient (NCG). The proposed technique produced higher recognition accuracy of 12.2% better than MBSGD, 27.2% better than SHF, 35.4% better than HF, 40.2% better than NCG and 32% better than SGD on an average when applied to 50,000 testing sample size.

Validation of an Improved Optimization Technique for Photovoltaic Modeling

Particle Swarm Optimization technique has been improved by fractional order calculus to be used for photovoltaic (PV) modeling. The modified technique which is called Fractional Order Darwinian Particle Swarm Optimization (FODPSO) has been constructed to estimate the optimal electrical parameters of PV modules. Single and double diode models have been used to designate the PV modules. FODPSO and PSO algorithms have been designed and applied on two different PV modules at different irradiances and temperatures. In order to validate the proposed modeling technique, Root Mean Square Error (RMSE) of the current, RMSE of power and Summation of the Individual Absolute Error (SIAE) results obtained using FODPSO and traditional Particle Swarm Optimization (PSO) algorithms have been compared. Minimum RMSE and SIAE have been achieved using the FODPSO technique. To verify the FODPSO results accuracy, accurate measurements of short circuit current, open circuit voltage, and maximum power, voltage at maximum power and current at maximum power have been performed for both PV modules. FODPSO-estimated results show excellent agreement with the experimental ones at different irradiances and temperatures.

Reliable E Nose System using the Improved Optimization Technique based ANN

(Since from last decade, there is a growing interest in a system that detects the pollutant gases and other environmental information is called Electronic Nose (E-Nose) networks. The gases such as methanol, Liquid Petroleum Gases, ammonia, etc. are harmful for human beings; therefore, such frailness required detecting automatically as well as safety alarm promoted in a specific field. The critical challenges of the E-nose system are efficient to detect with minimum error and overhead. In this paper, we targeted to design the optimized machine learning-based algorithm to detect and alert the pollutant gases, Humidity, O2 Level, and Air Temperature in the real-time datasets. We initiated E-nose design using Artificial Neural Network (ANN). Using essential ANN leads to poor accuracy and error rates, as they failed to select the best solutions during the training process. Thus, we next use the Particle Swarm Optimization (PSO) based ANN called ANN-PSO to improve the accuracy rate and error performances for E-Nose systems. Finally, the proposed Improved Optimization Technique based ANN (IOT-ANN) machine learning model designed and evaluated in current this research work. The IoT-ANN it is based on a bio-inspired algorithm to achieve reliable training during the E-Nose prediction.

A sliding-neural network control of induction-motor-pump supplied by photovoltaic generator

Energy production from renewable sources offers an efficient alternative non-polluting and sustainable solution. Among renewable energies, solar energy represents the most important source, the most efficient and the least expensive compared to other renewable sources. Electric power generation systems from the sun’s energy typically characterized by their low efficiency. However, it is known that photovoltaic pumping systems are the most economical solution especially in rural areas. This work deals with the modeling and the vector control of a solar photovoltaic (PV) pumping system. The main objective of this study is to improve optimization techniques that maximize the overall efficiency of the pumping system. In order to optimize their energy efficiency whatever, the weather conditions, we inserted between the inverter and the photovoltaic generator (GPV) a maximum power point adapter known as Maximum Power Point Tracking (MPPT). Among the various MPPT techniques presented in the literature, we adopted the adaptive neuro-fuzzy controller (ANFIS). In addition, the performance of the sliding vector control associated with the neural network was developed and evaluated. Finally, simulation work under Matlab / Simulink was achieved to examine the performance of a photovoltaic conversion chain intended for pumping and to verify the effectiveness of the speed control under various instructions applied to the system. According to the study, we have done on the improvement of sliding mode control with neural network. Note that the sliding-neuron control provides better results compared to other techniques in terms of improved chattering phenomenon and less deviation from its reference.