Optimal Parameter Values Research Articles

Prediction and optimisation of gasoline quality in petroleum refining: The use of machine learning model as a surrogate in optimisation framework

AbstractHardware‐based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number (RON) in the petroleum refining industry. Machine learning techniques are employed to predict the RON of integrated naphtha reforming and isomerisation processes. A dynamic Aspen HYSYS model was used to generate data by introducing artificial uncertainties in the range of ±5% in process conditions, such as temperature, flow rates, etc. The generated data was used to train support vector machines (SVM), Gaussian process regression (GPR), artificial neural networks (ANN), regression trees (RT), and ensemble trees (ET). Hyperparameter tuning was performed to enhance the prediction capabilities of GPR, ANN, SVM, ET and RT models. Performance analysis of the models indicates that GPR, ANN, and SVM with R2 values of 0.99, 0.978, and 0.979 and RMSE values of 0.108, 0.262, and 0.258, respectively performed better than the remaining models and had the prediction capability to capture the RON dependence on predictor variables. ET and RT had an R2 value of 0.94 and 0.89, respectively. The GPR model was used as a surrogate model for fitness function evaluations in two optimisation frameworks based on genetic algorithm and particle swarm method. Optimal parameter values found by the optimisation methodology increased the RON value by 3.52%. The proposed methodology of surrogate‐based optimisation will provide a platform for plant‐level implementation to realise the concept of industry 4.0 in the refinery.

Optical and morphological studies of Titania nanotubes by varying anodization parameters

In this study, the self-organized Titania (TiO2) was created by anodizing pure titanium in a mixture of ethylene glycol, ammonium fluoride, and distilled water using an electrochemical cell. This study aims to examine how changes in anodization parameters impact the morphology and optical properties of Titania. The nanotubes achieved 10 μm length by applying less ammonium fluoride (NH4F) electrolytes. The energy dispersive X-ray spectroscopy (EDX) reveals that the prepared samples are essentially composed of TiO2. Mixed phases of rutile and anatase are obtained by increasing the annealing temperature from 360 to 480 ℃ and crystalline size seems to be reduced by increasing anodization potential, calculated by X-ray diffraction analysis (XRD), diffuse reflectance spectroscopy (DRS) helps to identify the bandgap value and the minimum value attained at 2.56 eV without doping and the electron-hole recombination rates calculated through the intensity of photoluminescence (PL) analysis. Ultimately, the optimal values of the anodization parameters for the generation of the most effective sample are determined.

Testing the suitability of v-Support Vector Machine for hyperspectral image classification

ABSTRACT This research presents the novel application of v-Support Vector Machine (v-SVM) to hyperspectral image classification. The essence of this work is to test the suitability of v-SVM for hyperspectral image classification. The v-SVM provides an enhancement to the regularisation parameter in the classical Support Vector Machine (SVM). The regularisation parameter controls the trade-off between obtaining a high training error and a low training error which is the ability of the model to generalise the unseen data (or test data). The value of the regularisation parameter in the classical SVM ranges from 0 to + ∞ ; this makes it usually challenging to determine the most appropriate optimal regularisation parameter value. The invention of the v-SVM has made it easier to find an appropriate optimal regularisation parameter value since the regularisation parameter is narrowed down from a wide range of 0 to + ∞ to a narrow range of 0 to 1 . In this study, two hyperspectral images of Indian Pines region in Northwest Indiana, USA and University of Pavia, Italy are used as test beds for the experiment. The result of the experiment shows that the v-SVM performed fairly better than the classical SVM; however, it fell short of the notable conventional classifiers.

Absolute Value Inequality SVM for the PU Learning Problem

Positive and unlabeled learning (PU learning) is a significant binary classification task in machine learning; it focuses on training accurate classifiers using positive data and unlabeled data. Most of the works in this area are based on a two-step strategy: the first step is to identify reliable negative examples from unlabeled examples, and the second step is to construct the classifiers based on the positive examples and the identified reliable negative examples using supervised learning methods. However, these methods always underutilize the remaining unlabeled data, which limits the performance of PU learning. Furthermore, many methods require the iterative solution of the formulated quadratic programming problems to obtain the final classifier, resulting in a large computational cost. In this paper, we propose a new method called the absolute value inequality support vector machine, which applies the concept of eccentricity to select reliable negative examples from unlabeled data and then constructs a classifier based on the positive examples, the selected negative examples, and the remaining unlabeled data. In addition, we apply a hyperparameter optimization technique to automatically search and select the optimal parameter values in the proposed algorithm. Numerical experimental results on ten real-world datasets demonstrate that our method is better than the other three benchmark algorithms.

Development and optimization of general drying models for cod (Gadus spp.) using supercritical carbon dioxide

This study aimed to investigate the supercritical carbon dioxide (SC-CO2) drying of cod (Gadus spp.) by developing and optimizing a general mathematical drying model. We applied a central composite rotatable design (CCRD) considering the time (180–360 min), temperature (35–45 °C), and CO2 flow rate (15–25 kg/h) as independent factors, aiming at the reduction of moisture and water activity (aw) of Gadus morhua. Time and flow rate (FR) were the main factors affecting the moisture and aw responses. The mathematical models developed were validated with the accuracy and bias factors and generalized using Gadus chalcogrammus samples, resulting in the acceptable range of 1.003–1.121. The optimal parameter values for drying performance were 312.38 min, 45.38 °C, and 27.4 kg/h (R2 = 0.999). Our results suggest a promising application of SC-CO2 as a fish drying technology, describing a generalist-validated and optimized SC-CO2 drying model for Gadus spp. Industrial applicationThe present study collaborates with understanding supercritical carbon dioxide (SC-CO2) as an innovative fish drying technology at a laboratory scale. The optimal variables that were effective in reducing cod fillets water content were 312.38 min, 45.38 °C, and 27.4 kg/h, in which 26.4% moisture and 0.815 aw were predicted. Therefore, SC-CO2 is suggested to be a promising non-thermal food drying technology applied to fish products.

Optimum Parameters in Ultrasound Coherent Plane Wave Compounding for High LPWV Estimation: Validation on Phantom and Feasibility in 10 Subjects.

The aim of this study is to determine the optimum and fine values of the number and transmission angles of tilted plane waves for coherent plane-wave compounding (CPWC)-based high local pulse wave velocity (LPWV) estimation. A Verasonics system incorporating a linear array probe L14-5/38 with 128 elements and a pulsatile pump, CompuFlow1000, were used to acquire radio frequency data of 3, 5, 7, and 9 tilted plane wave sequences with angle intervals from 0° to 12° with a coarse interval increment step of 1°, and the angle intervals from 0° to 2° with a fine interval increment step of 0.25° from a carotid vessel phantom with the LPWV of 13.42 ± 0.90 m/s. The mean value, standard deviation, and coefficients of variation (CV) of the estimated LPWVs were calculated to quantitatively assess the performance of different configurations for CPWC-based LPWV estimation. Ten healthy human subjects of two age groups were recruited to assess the in vivo feasibility of the optimum parameter values. The CPWC technique with three plane waves (PRF of 12 kHz corresponding to a frame rate of 4000 Hz) with an interval of 0.75° had LPWVs of 13.52 ± 0.08 m/s with the lowest CV of 1.84% on the phantom, and 5.49 ± 1.46 m/s with the lowest CV of 12.35% on 10 subjects. The optimum parameters determined in this study show the best repeatability of the LPWV measurements with a vessel phantom and 10 healthy subjects, which support further studies on larger datasets for potential applications.

Solar adsorption cooling system operating by activated–carbon–ethanol bed

One efficient way to convert small thermally energized into effective cooling is through adsorption cooling technology, which increases energy efficiency and reduces environmental pollution. This study's primary goal is to hypothetically examine the thermal coefficient of performing the solar adsorptive refrigerator machine operated with an activating carbon/Ethanol operating dual. The impact of different operating situations and design factors on the machine's performance is inspected and evaluated. The present double-bed solar energy adsorptive-cooler unit is modeled by thermodynamic methodology. Then, it was analyzed to evaluate its effectiveness work under Baghdad climate conditions. For the current study, the two-bed solar adsorption cooling unit with 0.5 kW capacity input heat 11893 that operates at 5 °C for the evaporator and 45 °C for the condenser is presented. The Engineering-Equation-Solver (EES) simulation program was created and used to solve the modeling equations that predict the optimal cycle performance and evaluate the optimum reasonable values of the operation parameters of the proposed system. The pressure range for the refrigeration cycle is 2.408 kPa for the evaporation state and 23.14 kPa for the condensation state. The findings demonstrate that an optimum coefficient of performance (COP) is 0.702 at 95 °C, a 20% performance increase, which generates 39.4 of cooling water. It produced 1 kg of chilled water for 2.463 kg of activated carbon at a temperature of 5°C. The improved solar-powered adsorption systems and refrigeration technologies are appealing substitutes that can satisfy energy demands in addition to meeting needs for cooling, ice production, air conditioning, and refrigeration preservation and safeguarding of the environment with Iraq's climate conditions.

Calibration and validation of DEM parameters of food waste with high total solid content (≥20%) using physical experiments and EDEM simulations

Since discrete element method (DEM) parameters are important for the calculation accuracy of EDEM simulations, the DEM parameters of food waste with high total solid content (≥20%) are calibrated and validated by experiments and simulations in this paper. The physical properties of food waste are obtained by physical experiments, and then the optimal values of DEM model parameters recommended for food waste can be obtained from the GEMM database. Finally, the repose angle and the average torque obtained by EDEM simulations are compared with the experimental results to validate the calibrated DEM parameters of food waste. The average relative error of the repose angle between the experimental values and the simulation values is 3.23%, and the average relative error of the average torque value is 5.29%. That means the DEM parameters of food waste calibrated in this paper are correct and can be applied for simulating the mixing performance of food waste with high total solid content (≥20%).

Fractional-Order Equivalent-Circuit Model Identification of Commercial Lithium-Ion Batteries

The precise identification of electrical model parameters of Li-Ion batteries is essential for efficient usage and better prediction of the battery performance. In this work, the model identification performance of two metaheuristic optimization algorithms is compared. The algorithms in comparison are the Marine Predator Algorithm (MPA) and the Partial Reinforcement Optimizer (PRO) to find the optimal model parameter values. Three fractional-order (FO) electrical equivalent circuit models (ECMs) of Li-Ion batteries with different levels of complexity are used to fit the electrochemical impedance spectroscopy (EIS) data operating under different states of charge (SoC) and different operating temperatures. It is found that there is a tradeoff between ECM complexity, identification accuracy, and precision.

DL-RSM: Deep learning-integrated Response Surface Methodology for positive and negative-ideal environmental conditions estimation for crop yield

Understanding the most suitable and unsuitable environmental scenarios for crop production is critical to ensuring food security and sustainability. In this direction, the presented work proposes a novel approach, DL-RSM, to identify the optimized levels of environmental factors for wheat yield by integrating the Deep Learning technique with Response Surface Methodology (RSM). The uniqueness of this approach lies in its utilization of a 1-Dimensional Convolutional Neural Network model for response surface modelling. Additionally, the study has investigated the combination of numerical methods-based partial derivative calculation with a deep learning model. Specifically, our approach incorporates a symmetric difference quotient-based method for calculating partial derivatives, ultimately leading to the determination of optimal parameter values. Besides, multiple positive and negative ideal solutions are obtained from the modelled response surface using a modified gradient-based method. The effectiveness of our approach is illustrated through a case study on finding the optimized parameter values for the district-wise wheat yields of the Indo-Gangetic plains of India. The study results demonstrate the combinations of monthly rainfall levels and min. and max. temperature variations correspond to maximizing and minimizing wheat yield in the target region. The presented results provide significant insights into the relationship between wheat yields and the considered environmental factors that have not been sufficiently covered in the existing literature. The proposed methodology can benefit agricultural scientists, policymakers and farmers by offering an innovative approach to maximize crop yield and minimize resource utilization. Moreover, the study findings provide novel insights into the development of similar optimization strategies for other crops and systems.

Quenching Process Optimization of Leaf Springs as Tengkuit Material using the Taguchi Method Approach

Tengkuit is a traditional tool used by farmers to care for katuk, chilies, lumai, and others. The problem that farmers complain about is frequent wear and tear and chipping, which according to farmers and craftsmen must maximize quality and this must increase production costs and farmers' expenses. As time goes by, innovations have been made by humans to increase the durability of an object or material by increasing its hardness to save or reduce production costs. This research used qualitative approach with the taguchi method. The Taguchi method is a new engineering technique that aims to improve product and process quality while minimizing costs and resources. Taguchi's experimental design is more efficient because it allows searches involving many factors and numbers, and has good large-scale violent response. The maximum check value is the optimal parameter value for the tengkuit making process. Temperature and cooling media are very influential factors in testing hardness for making tengkuit. The results of the hardness research were shown as the lowest hardness value at a temperature of 600°C, with well water cooling media with a value of 24.3 HRC, and the highest hardness value at a temperature of 800°C. with bromus oil cooling media at a temperature of 700 ° C and a hardness value of 42.9HRC.

Finite element analysis of wire EDM process parameters on performance characteristics of Al–SiC hybrid composite with micro and nano reinforcements

Aluminum Hybrid Composites (AHC) microchannels find extensive applications in aerospace, refrigeration and air conditioning (RAC), microelectronics, automotive, biomedical, and marine industries due to their improved mechanical properties. The combination of lower density, high hardness, elevated thermal and electrical conductivity, superior elasticity, and heightened oxidizing ability at low temperatures makes machining aluminum hybrid composites challenging through orthogonal methods in an open environment. Therefore, it is imperative to investigate the impact of various machining conditions to determine optimal parameter values for microchannel fabrication in aluminum hybrid composite materials. This study aims to identify the ideal machining parameters for Al–SiC micro-SiC nano hybrid composites using wire electrical discharge machining. Four distinct process parameters (pulse on time, gap voltage, wire tension, and wire feed rate) and two response parameters (material removal rate (MRR) and surface roughness (SR)) were considered for process optimization. Additionally, finite element modeling was conducted for the aforementioned process, and the results were compared with experimental outcomes. Single-variable optimization yielded maximum MRR at 125 tons (μs), 25 SV (volt), 12 WT (kgf), and 2 WFR (m/min), and minimum SR at 125 tons (μs), 10 SV (volt), 12 WT (kgf), and 4 WFR (m/min). Single-variable optimization for simulations resulted in maximum MRR at 125 tons (μs), 40 SV (volt), 4 WT (kgf), and 6 WFR (m/min), and minimum SR at 110 tons (μs), 10 SV (volt), 12 WT (kgf), and 2 WFR (m/min). Precise modeling aids in minimizing unnecessary machining, enabling exploration of a broader range of experimental results, thereby promoting environmentally friendly and sustainable machining practices.

Иерархическая модель оптимизации технологических параметров комплекса рабочих переходов для процесса механической обработки

Optimization of the parameters of the product manufacturing process is one of the key tasks of technological preparation of production. The technological process of mechanical processing has a complex hierarchical struc-ture. The effectiveness of optimizing the manufacturing process of a product directly depends on the level of its detail and the optimal choice of targets and control parameters. In this case, the technological process of mechanical processing, as an object of control, can be described in the form of a hierarchical model. Thus, the task of optimizing the technological process of mechanical processing is to determine the optimal values of control parameters for each structural element (intermediate state of the control object) of the hierarchical control model. The aim of the work is to develop a hierarchical model for optimizing the parameters of a complex of working transitions for machining operations. The structure of the hierarchical model of the product manufacturing process on metal-cutting machines is described. Within the framework of the developed model, five control levels are identified, control parameters for individual structural elements of the model are defined, as well as the relationship between them. For the intermediate states of the control object (structural elements), a description of single and vector optimization criteria is presented. The practical implementation of the developed control model is presented using the example of optimizing the technological parameters for the “Bushing” part. The application of the developed control model will increase the efficiency of the technological process of manufacturing products on metal-cutting machines by optimizing technological parameters based on a multi-criteria analysis at the stage of technological preparation of production.

Evaluation of the fully coupled WRF and WRF-Hydro modelling system initiated with satellite-based soil moisture data

ABSTRACT This study comparatively evaluates the skill of the (un)coupled Weather Research and Forecasting (WRF) – WRF-Hydro system supported with satellite-based soil moisture initiation in long-term simulations of hydrometeorological variables under humid and semi-arid climate conditions. Prior to the coupling, the WRF-Hydro model is calibrated using streamflow data at different data scales (i.e. hourly and daily) to discuss the influences of the changing temporal resolution of the reference dataset on the optimum parameter values. According to the results, fully coupled models correct the location and magnitude of the occurrence of hourly extreme precipitation. Changing the initial soil moisture data source affects the terrestrial water cycle elements in the semi-arid region (i.e. Mediterranean (MED)) more than in the humid area (i.e. Eastern Black Sea (EBS)), especially in spring and summer. The initiation of the coupled model with satellite-based soil moisture improves the streamflow outputs after a reasonable spin-up period for the MED region.

Performance analysis of ARIMA Model for wind speed forecasting in Jerusalem, Palestine

Palestine lacks sufficient conventional energy sources that meet the daily needs of the Palestinian people, and consequently, it heavily relies on neighboring countries for its supply with energy compensations. Wind energy is recognized as an abundant, effective, and eco-friendly power source, but it poses several challenges in harnessing due to the inherent variability of wind characteristics. The main objective of this research study is to delve into the wind energy landscape in Palestine, and to offer some insights into the feasibility of wind speed forecasting for implementing sustainable energy solutions, with a special focus on ARIMA; a widely used statistical method for time series forecasting. It specifically explores the potential of using ARIMA models to forecast wind speed using a data captured from a meteorological station located in east Jerusalem, Palestine for a duration of 2 years—January 1, 2021 to December 31, 2022. To find the optimal values of ARIMA parameters (p, d, q) for the considered study site, a set of experiments were conducted and the model's forecasting accuracy was evaluated using three metrics: RMSE, MAE, and the coefficient of determination (R2). The results have shown that ARIMA (21,2) emerges as the most accurate structure with an input period that demonstrates superior estimation with minimal RMSE (1.74), minimal MAE (1.58) and higher R2 (0.76) values. This means that the optimal estimation is achieved when an autoregressive process is based on the previous two lagged observations and the moving average process incorporates the dependency between the observation and the residual error from a second-order moving average applied to the lagged observations. These findings give valuable insights into the feasibility and precision of wind speed forecasting models for sustainable energy solutions, and emphasize the potential for harnessing wind energy in the region as clarified by ARIMA forecasting accuracy.

Optimizing tensile strength and energy consumption for FDM through Mixed-Integer Nonlinear Multi-objective optimization and design of experiments

This study presents a methodology for optimizing key parameters of a fused deposition modeling (FDM) printer to minimize energy consumption (EC) while exceeding a specified tensile strength (TS) threshold. Employing Design of Experiments (DoE) with Taguchi and Response Surface analysis, we identify influential parameters affecting TS and EC. A Mixed-Integer Nonlinear Multi-Objective Optimization model is then utilized to balance TS and EC, resulting in optimal parameter values. Validation using fabricated specimens demonstrates less than 5 % error in Tensile Strength and less than 2 % error in Energy Consumption, confirming the efficacy of the proposed methodology.

The Optimization of the Geometry of the Centrifugal Fan at Different Design Points

The optimization of the geometry of a centrifugal fan is performed at maximum power and high-efficiency design points (DPs) to improve impeller efficiency. Two design variables defining the shape of fan blade are selected for the optimization. The optimal values of the geometry parameters of the impeller blades are identified by employing virtual flow simulations. The results of virtual experiments indicate the influence of the parameters of the blade geometry on its efficiency. With the optimization of impeller blade geometry, the efficiency of the fan is improved with respect to the reference model, as confirmed by comparing the performance curves. Herein, we discuss the results obtained in virtual tests by identifying the influence of DPs on the performance characteristics of centrifugal fans.

Vibration mitigation of spar-buoy floating wind turbines using a nonlinear energy sink

This work investigates the possibility of mitigating the wind-wave exerted vibration of floating offshore wind turbines using a nonlinear energy sink (NES). The vibration mitigation of floating wind turbines using passive linear control strategies have been intensively investigated, however, the effectiveness of the linear absorbers only works in a narrow frequency band, resulting in limited vibration suppression performance. The NES comprising a mass, a damper and a cubic stiffness element has the ability to mitigate vibrations in a broadband frequency range, which has been adopted in multiple engineering structures with significant performance benefits identified. In this paper, taking spar-buoy floating wind turbines as prototype structure, a simplified model with a NES mounted in nacelle subjected to wind and wave loads is presented. The harmonic balance method is then employed to analyse its nonlinear responses to the influence of NES parameters, together with the pseudo-arc-length method. The optimum NES parameter values are subsequently identified. Results show that the performance improvement can be up to 74.2% and 14.0%, respectively, comparing to that with no absorber and the TMD. Furthermore, to demonstrate the vibration suppression ability of the optimised NES in more realistic spar-buoy wind turbines, Orcaflex-the world’s leading package for the dynamic analysis of offshore systems, is adopted as the simulation tool. The spar-buoy wind turbine as well as the NES are established in Orcaflex and it is demonstrated that the performance benefits with the NES preserve under various loading conditions.

Adaptive continuous barrier function-based super-twisting global sliding mode stabilizer for chaotic supply chain systems

Supply chains face various uncertainties due to the dynamic and unstable nature of today's worldwide market. Introducing these uncertainties to both the upstream and downstream elements of supply chains adds complexity and nonlinearity to them. This paper investigates the dynamic behavior of a chaotic three-echelon supply chain system and designs a finite-time global nonlinear super-twisting sliding mode controller based on an adaptive continuous barrier function technique for stabilizing the defined system under external disturbances and model uncertainties. This method dynamically adapts the controller parameters according to the system's current state using parameter adaptation algorithms. The adaptive barrier function is used to ensure the system's stability, while the super-twisting algorithm is used to speed up the convergence rate and robustness of the system. Moreover, the genetic optimization algorithm has been employed to attain the optimal values of parameters, thereby enhancing the performance of the designed controller. Ultimately, the effectiveness of this research's proposed approach has been evaluated through simulation in the MATLAB-Simulink environment and experimental testing using the Baseline Real-Time Speedgoat test platform. These analyses are paramount and helpful for strategic decision-makers because they allow visualization and control of the states/dynamics of the entire supply chain. The internal parameters of the supply chain are used as control parameters, and different significant states are discovered to achieve synchronization.

Innovative solar distillation system with prismatic absorber basin: Experimental analysis and LSTM machine learning modeling coupled with great wall construction algorithm

Enhancing the vaporization surface area of the solar distillers through the implementation of a novel configurations based solar distiller represents a cost-efficient strategy for maximizing the distilled water output of conventional solar stills. The study introduces a pioneering wicked prismatic-shaped solar distiller equipped with wick materials and feed spaying nozzles, aimed at augmenting vaporization rates inside the still trough and, consequently, increasing the yield of freshwater. Two solar distillers with double slope covers are constructed and tested include a modified solar still with a prismatic basin, two incline covers, and spraying nozzles (MSS) and a reference double slope solar still (RSS). Furthermore, we have constructed a hybrid artificial intelligence framework, integrating a long short-term memory (LSTM) neural network fine-tuned through the utilization of great wall construction algorithm (GWCA). This model has been designed for the purpose of forecasting both the saltwater temperature and the associated freshwater product within the two examined solar distillers, whereas, the time, solar flux, wind velocity, and ambient temperature are considered as inputs. GWCA is effectively employed to optimize the LSTM model by determining the optimal parameter values to enhance its performance. The experimental results revealed that the daily freshwater production for the MSS reached 7.94 kg/m²/day, while the RSS achieved 5.31 kg/m²/day. This represents a substantial 49.53% improvement when compared to the RSS. Additionally, the daily energy efficiency of the MSS and RSS was assessed at 57.40% and 39.80%, respectively, whereas the daily exergy efficiency was 3.80% and 2.20%, respectively, signifying a notable 44.23% and 72.74% increase the energetic and exergetic efficiencies over RSS. Furthermore, the prediction findings demonstrated that during the testing phase, the coefficient of determination for saltwater temperature prediction of the MSS was calculated at 0.996 for LSTM-GWCA and 0.963 for LSTM. In the case of freshwater product prediction, these values were 0.983 for LSTM-GWCA and 0.922 for LSTM, respectively.