Optimal Parameter Values Research Articles

Load-aware continuous-time optimization for multi-agent systems: toward dynamic resource allocation and real-time adaptability

In the realm of next-generation mobile communication networks, characterized by dynamic and evolving workloads, the efficient resource allocation becomes paramount for achieving optimal performance. This paper addresses the intricate challenges of multiagent resource management through the integration of stochastic learning automata and continuous-time optimization techniques, presenting an innovative solution for real-time adaptability and dynamic load balancing. The central aim is to achieve optimal values for local time-varying cost functions while taking into account resource constraints and the feasibility of local allocation. In the context of multi-agent resource allocation systems, where conditions vary over time, the algorithm exhibits a dynamic adaptability, continuously adjusting to the evolving optimal solutions tailored for each agent. Utilizing stochastic learning automata, the algorithm effectively addresses the time-varying load-balancing function in response to dynamic user demands. Furthermore, we introduced an scalable solution for mobility robustness optimization based on deep reinforcement learning (DRL-MRO). This method dynamically identifies network-wide optimal parameter values across the entire network to accommodate diverse mobility patterns, ensuring the effectiveness of load balancing parameters that seamlessly adapt to the dynamic configuration of the network. The overarching goal is to maintain a consistent quality of service level for each agent, thereby enhancing the overall system performance. Simulation outcomes unequivocally affirm the superior performance of the proposed load balancing approach, rooted in continuous-time optimization and stochastic learning automata, surpassing existing schemes across diverse system configurations.

A piezoelectric transducer for bone conduction implants designed using finite element analysis

AbstractThis study describes the design of a transducer for bone conduction implants that combines a piezoelectric element and a displacement amplifier. To develop a displacement amplifier with the maximum possible amplification ratio, theoretical analysis was performed to derive the parameters that affect the amplification ratio. Parametric sweep analysis was conducted to calculate the amplification ratio according to changes in the various parameters. The resulting optimal displacement amplifier afforded the maximum amplification ratio when the beam thickness, depth, inclination angle, and length were 0.15 mm, 1.2 mm, 6.5°, and 4 mm, respectively. The displacement amplifier with these optimal parameter values amplified the piezoelectric element displacement by approximately 7.7‐fold. Frequency characteristic analysis by weight was performed to derive a mechanical resonance location with frequency characteristics that appropriately compensated for hearing loss. As a result of this analysis, when a weight of 0.7 g was applied to the displacement amplifier, the mechanical resonance was that required for bone conduction implant transducers.

Investigation of longitudinal dynamics of vehicles in disordered traffic

Vehicles in developing countries have wide variations in their static and dynamic characteristics, and drivers tend to not follow lane discipline. Models for driving behavior under such disordered traffic conditions need to include the vehicle dynamics and their interactions with the surrounding environment. Calibration of those models is necessary to evaluate their predictive power and suitability for analyzing traffic flow under disordered traffic. The present study aims to calibrate a longitudinal dynamics model, the High-Speed Social-Force Model (HSFM) using a vehicle trajectory dataset collected from Chennai city. The HSFM was calibrated by minimizing the deviations between the simulated and observed longitudinal coordinates of vehicles using a genetic algorithm. The observed and simulated vehicle trajectories were compared using a goodness of fit function of the positions. The convergence of the objective function has been illustrated with the help of fitness landscapes. The calibration errors were found to be within the acceptable range and the optimal parameter values were found to be consistent. The outcomes of the study indicate that the model can capture the influence of non-overlapping leaders under disordered traffic conditions.

Identification of mine water source based on TPE-LightGBM

Mine water inrush is a serious threat to mine safety production. It is very important to identify water inrush source types quickly to prevent and control water damage. In this study, the aqueous chemical components Na+ + K+, Ca2+, Mg2+, Cl−, SO42− and HCO3− of different aquifers in Pingdingshan coalfield were selected as the characteristic values, and the Surface water, Quaternary pore water, Carboniferous limestone karst water, Permian sandstone water, and Cambrian limestone karst water were used as the labels. An intelligent water source discrimination model is proposed by combining data mining, classification models, and reinforcement learning. As outlier data in the samples may interfere with the model recognition ability, the data distribution range was analyzed using box plots, and 20 groups of abnormal samples were excluded. The processed water chemistry data were divided into 80% learning samples and 20% test samples, and the learning samples were fed into a light gradient boosting machine (LightGBM) for training. The tree-structured parson estimator (TPE) obtains the optimal values of the main parameters of LightGBM in a very short time. Substituting the hyperparameters back into the model yields a 13.9% improvement in the accuracy of the model, proving the effectiveness of the TPE algorithm. To further validate the performance of the model, TPE-LightGBM is compared and analyzed with a Random Search-Multi Layer Perceptron Machine (RS-MLP) and Genetic Algorithm-Extreme Gradient Boosting Tree (GA-SVM). The accuracy of TPE-LightGBM, RS-MLP, and GA-SVM is 0.931, 0.759, 0.724 in that order, and the generalization error RMSE is 0.415, 1.05, and 1.313 in that order. The results show that TPE-LightGBM is more advantageous in water source identification and is more resistant to overfitting. By calculating and comparing the information gain of each variable, the contribution of Ca2+ is the highest, so it is necessary to pay attention to the change in Ca2+ concentration. TPE-LightGBM’s high accuracy and generalization ability have a good prospect for the identification of sudden water source types.

Optimization of Antifungal Properties of Hop Cone Carbon Dioxide Extracts Based on Response Surface Methodology.

Response surface methodology (RSM) was employed to optimize the process parameters of the supercritical carbon dioxide extraction of hop cones in terms of their antifungal properties against Fusarium culmorum and Aspergillus niger. The effects of temperature (40-50 °C), pressure (200-300 bar), and CO2 consumption (25-75 kgCO2/kg) on the extraction yield, content of α- and β-acids, as well as pathogens' growth inhibition were investigated. Both pressure and CO2 consumption had a significant effect on antifungal properties. It was observed that the best results for antifungal properties were obtained when hop cones were extracted with pure carbon dioxide at the temperature of 50 °C, under the pressure of 300 bar with CO2 consumption at the level of 75 kgCO2/kg of feed for extraction. The highest antifungal properties of hop cone supercritical carbon dioxide extracts were analyzed as 100% for Fusarium culmorum and 68% for Aspergillus niger, calculated as the growth inhibition of tested pathogens. The aim of the study was to determine the optimum values of extraction parameters to achieve the maximum response and enable us to investigate the interaction of these parameters on the antifungal properties of hop cone extracts.

Tuning pore size and density of rigid polylactic acid foams through thermally induced phase separation and optimization using response surface methodology

Rigid polylactic acid (PLA) foams fabricated via thermally induced phase separation (TIPS) utilizing a ternary solution of PLA, Tetrahydrofuran (THF), and water. The PLA gels were stabilized mechanically by the substituting of the THF/water solvent mixture with ethanol as non-solvent and subsequently vacuum dried. A comprehensive characterization of PLA foams was achieved by Scanning Electron Microscopy (SEM), X-ray Diffractometry (XRD) and Brunauer–Emmett–Teller (BET) analyses. The BET area obtained in the PLA foam is up to 18.76 m2/g. The Response Surface Methodology (RSM) was utilized to assess the impacts of four independent variables (polymer concentration, solvent composition, quench temperature, and aging time) on the pore size and density of PLA foam. The experimental findings demonstrated that the fabrication parameters could be fine-tuned to govern the morphology of the pores, comprising their size and density. The optimal values of parameters for cell size were identified by RSM to be 8.96 (wt%), 91.60 (w/w), 5.50 °C, and 3.86 h for the optimum cell size of 37.96 µm (37.78 by Genetic Algorithm). Optimum density by RSM 88.88 mgr/cm3 (88.38 mgr/cm3 by Genetic Algorithm) was obtained at 5.00 (wt%), 89.33 (w/w), 14.40 °C and 2.65 h.

Important Predictive Factors for the Prognosis of Patients With Peritoneal Metastasis of Gastric Cancer.

This study investigated predictive factors for patients with peritoneal metastases of gastric cancer (PMGC) who underwent conversion cytoreductive surgery (C-CRS) and hyperthermic intraperitoneal intraoperative chemotherapy (HIPEC) after responding to induction chemotherapy (laparoscopic HIPEC [LHIPEC]) followed by concomitant systemic and intraperitoneal chemotherapy (bidirectional intraperitoneal and systemic chemotherapy [BIC]). Diagnostic laparoscopy was performed for 62 patients with PMGC between January 2017 and December 2022. The patients underwent LHIPEC and BIC induction chemotherapy using intraperitoneal docetaxel (30mg/m2) and cisplatin (30mg/m2), and intravenous chemotherapy for three cycles. The predictive parameters for progression-free and overall survival were analyzed using Kaplan-Meier and Cox regression analyses. The optimal cutoff values for Ki-67 parameters were assessed using receiver operating characteristic curve analysis. The study retrospectively examined 36 (58 %) of 62 patients who responded to induction therapy and underwent C-CRS or HIPEC. A Ki-67 index lower than 10 (p = 0.000), lymph node involvement (LNI) less than 2 (p = 0.039), and an omental lesion size score lower than 0.5 cm (p = 0.002) were predictive of recurrence-free and overall survival in addition to completeness of cytoreduction and the peritoneal cancer index. Cox regression analysis showed that the independent factors associated with recurrence-free survival were decreased Ki-67 expression (≥10 % vs <10 %) (hazard ratio [HR] 4.7; 95 % confidence interval [CI] 1.6-5.210; p = 0.020) and LNI higher than 2 (HR 1.92; 95% CIS 0.923-4.0; p = 0.023). Lymph node involvement and decreased Ki-67 expression are independent predictive factors of recurrence-free survival for patients with PMGC after induction chemotherapy.

A novel analytical approach to vertical dynamic vibration of pipe‐pile groups considering soil‐plug effect

AbstractA novel pile‐to‐pile interaction model for pipe piles that considers the soil‐plug effect is established based on the Novak plane‐strain soil model. Subsequently, the pipe‐pile dynamic interaction factor and dynamic impedance of pipe‐pile groups are derived using the superposition method. The accuracy of the proposed analytical solution is confirmed by comparison with experimental results and existing solutions. Finally, numerical results are obtained to study the influence of the soil‐plug height, shear modulus of the soil, pile spacing, and pile outer radius on the pipe‐pile dynamic interaction factor and dynamic impedance of pipe‐pile groups. The results indicate that the effect of the soil‐plug height on the pipe‐pile dynamic interaction factor and dynamic impedance is significant, whereas the shear modulus of the soil plug has virtually no effect. The proposed analytical solution can be applied to determine the optimal values of pile and soil parameters during the dynamic design of pipe‐pile groups.

Determining the optimal dome shape using a novel variable slippage coefficient non‐geodesic

AbstractThe research of high‐performance composite pressure vessels in practical application has become the focus of attention, and fiber paths play an important role in the structural performance of composite pressure vessels. Therefore, a method is proposed for determining the optimal geometric parameters of the pressure vessel head shape under a novel variable slippage coefficient non‐geodesic(NVSCNG). First, NVSCNG is used as the dome fiber path design mode, and the classical lamination theory is used to construct a dome stress field model. In addition, on the basis of the Tsai‐Wu failure criterion, the minimum laminate thickness is determined to satisfy the strength constraints. Second, the performance factor (pressure*volume/weight) is used to calculate the structural performance of the dome with variable geometric parameters under the novel variable slippage coefficient fiber path. Moreover, the stability constraints are considered to determine the optimal geometric parameter values of the dome. Finally, the structural properties, stress response and thickness were analyzed under two types of fiber paths based on the optimal dome profile. Results show that compared to the conventional variable slippage coefficient, the performance factor can be maximally improved by nearly 60.94% using the novel variable slippage coefficient, and the thickness of the composite layer at the equator of the dome can be maximally reduced by nearly 34.88%. In addition, the research shows that NVSCNG can improve the structural performance of the dome and realize the lightweight of dome components, which are greatly important in guiding the design of fiber paths for pressure vessels.Highlights No unconstrained linear problem between NVSCNG and initial winding angle. Optimal dome profile geometry parameters are m = 0.8, rc = 0.5. The dome structural failure is controlled by the longitudinal stresses. Winding angle distribution has a strong influence on dome stress distribution. Higher dimensionless performance factor and lighter mass for NVSCNG.

Meta-heuristic algorithms for integrating manufacturing and supply chain functions

This study proposes an integrated framework that incorporates essential manufacturing and supply chain functions. These functions encompass process planning, scheduling, due-date assignment, and delivery optimization. The objective of this integrated approach is to achieve multiple benefits, including balanced workload distribution, enhanced company performance, generation of more realistic planning schedules, and ultimately, the achievement of shorter due dates. As a result, the overall efficiency of operations is substantially improved, with approximately a 50 % increase over isolated function management. Additionally, the isolated integration of the delivery function within systems comprising three integrated functions has been found to improve efficiency by 18%. The study employs various heuristic techniques, including genetic algorithms, simulated annealing, random search, hybrid search, and evolutionary strategy, to assess the optimal solution method and rules for these functions. The Taguchi technique is employed to ascertain the optimal values for critical parameters, such as population size, mutation rate, crossover points, and random search rate. Among the solution methods investigated, genetic algorithms consistently yielded superior results Additionally, the weighted slack rule consistently exhibited notable effectiveness compared to other due-date assignment rules. Similarly, the savings algorithm outperformed other delivery optimization rules. However, it is important to note that among the scheduling rules evaluated, none has emerged as dominant.

SVM Optimization with Grid Search Cross Validation for Improving Accuracy of Schizophrenia Classification Based on EEG Signal

The advantage of the Support Vector Machine (SVM) is that it can solve classification and regression problems both linearly and non-linearly. SVM also has high accuracy and a relatively low error rate. However, SVM also has weaknesses, namely the difficulty of determining optimal parameter values, even though setting exact parameter values affects the accuracy of SVM classification. Therefore, to overcome the weaknesses of SVM, optimizing and finding optimal parameter values is necessary. The aim of this research is SVM optimization to find optimal parameter values using the Grid Search Cross-Validation method to increase accuracy in schizophrenia classification. Experiments show that optimization parameters always find a nearly optimal combination of parameters within a specific range. The results of this study show that the level of accuracy obtained by SVM with the grid search cross-validation method in the schizophrenia classification increased by 9.5% with the best parameters, namely C = 1000, gamma = scale, and kernel = RBF, the best parameters were applied to the SVM algorithm and obtained an accuracy of 99.75%, previously without optimizing the accuracy reached 90.25%. The optimal parameters of the SVM obtained by the grid search cross-validation method with a high degree of accuracy can be used as a model to overcome the classification of schizophrenia.

Enhancing Energy Harvesting Efficiency of Flapping Wings with Leading-Edge Magnus Effect Cylinder.

According to the Magnus principle, a rotating cylinder experiences a lateral force perpendicular to the incoming flow direction. This phenomenon can be harnessed to boost the lift of an airfoil by positioning a rotating cylinder at the leading edge. In this study, we simulate flapping-wing motion using the sliding mesh technique in a heaving coordinate system to investigate the energy harvesting capabilities of Magnus effect flapping wings (MEFWs) featuring a leading-edge rotating cylinder. Through analysis of the flow field vortex structure and pressure distribution, we explore how control parameters such as gap width, rotational speed ratio, and phase difference of the leading-edge rotating cylinder impact the energy harvesting characteristics of the flapping wing. The results demonstrate that MEFWs effectively mitigate the formation of leading-edge vortices during wing motion. Consequently, this enhances both lift generation and energy harvesting capability. MEFWs with smaller gap widths are less prone to induce the detachment of leading-edge vortices during motion, ensuring a higher peak lift force and an increase in the energy harvesting efficiency. Moreover, higher rotational speed ratios and phase differences, synchronized with wing motion, can prevent leading-edge vortex generation during wing motion. All three control parameters contribute to enhancing the energy harvesting capability of MEFWs within a certain range. At the examined Reynolds number, the optimal parameter values are determined to be a∗ = 0.0005, R = 3, and ϕ0 = 0°.

Effects of Selective Laser Melting Process Parameters on Structural, Mechanical, Tribological and Corrosion Properties of CoCrFeMnNi High Entropy Alloy

The structural, tribological, mechanical, corrosion, and other properties of materials produced by laser-based powder bed fusion additive manufacturing methods are significantly affected by production parameters and strategies. Therefore, understanding and controlling the effects of the parameters used in the manufacturing process on the material properties is extremely important for determining optimum production conditions and for saving time and materials. This study aimed to determine the optimal laser parameter values for CoCrFeMnNi high-entropy alloy powders using the selective laser melting (SLM) method. The layer thickness was kept constant during experimentation. 5 different laser powers and 10 varying laser scanning speeds were tested, with hatch spacing from 30 to 90%. After determining the optimal laser parameters for SLM, prismatic samples were fabricated in different build orientations (0°, 45°, and 90°), and subsequently, their structural, mechanical, tribological, and corrosion properties were compared. Melt pool morphology could not be obtained at 20—40 and 60W laser powers and at all laser scanning speeds used at these laser powers. At 100 W laser power, 600 mm/s laser scanning speed, and 70% hatch spacing parameters, an ultimate tensile stress of 550 MPa and elongation of 48% were obtained. Among the samples produced in different build orientations, the sample produced with a 0° build orientation exhibited the highest relative density (99.94%), the highest microhardness (201.2 HV0.1), the lowest friction coefficient (0.7025), and the lowest wear and corrosion rates (0.7875 mpy). Additionally, SLM parameters were evaluated to have a significant impact on the performance of all properties of the samples.Graphical

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.