Mathematical Regression Model Research Articles

Energy Efficiency and Mathematical Modeling of Shrimp Pond Oxygenation: A Multiple Regression Experimental Study

Aquaculture is one of the key economic activities to reduce food shortages worldwide. Water recirculation systems using pumps are crucial to maintain oxygenation and water quality, consuming about 35% of the total energy in this economic activity. This research proposes a multiple linear regression mathematical model to optimize oxygenation systems in intensive shrimp aquaculture by reducing energy consumption and minimizing water changes in ponds. The proposed model is key to optimizing the operation of pumping systems, allowing us to significantly reduce water turnover without compromising dissolved oxygen levels as a function of key variables such as water turnover volume, biomass, solar radiation (0–1200 W/m2), water temperature (20 °C–32 °C), phytoplankton levels (0–1,000,000 cells/ml), zooplankton (0–500,000 cells/ml), and wind speed (0–15 m/s). These variables are integrated into the model, managing to explain 94.02% of the variation in dissolved oxygen, with an R2 of 92.9%, which adjusts the system conditions in real time, reducing the impact of environmental fluctuations on water quality. This leads to an estimated annual energy savings of 106,397.5 kWh, with a total consumption of 663.8 MWh. The research contributes to the development of a mathematical approach that not only improves oxygenation prediction, but also minimizes the use of water resources, improving the sustainability and profitability of shrimp farming systems, and is a robust tool that maximizes operational efficiency in intensive aquaculture, particularly where water and energy management are critical.

Model study on transmission loss of the split-stream rushing exhaust muffler for diesel engine

Numerical simulation was carried out on the transmission loss of the split-stream rushing exhaust muffler, and the accuracy of the simulation method was verified through experiments. Taking the transmission loss as the response value, the experiment was designed by using Box-Behnken module of Design Expert software. A mathematical regression model of transmission loss with experiment factors was established by using the Box-Behnken experimental design scheme, the experiment factors include the diameter of the interior pipe, the shape of the rushing hole, the center distance of the rushing hole, the cone angle of the interior pipe as well as the number of rushing holes, and the mathematical regression model's significance was tested. The response curvatures of second order interaction to the transmission loss by different variables were obtained and the interaction relationships among variables were analyzed. The results showed that the diameter of the interior pipe and the center distance of the rushing hole are the main factors that affect the transmission loss. The transmission loss increases with the increase of the diameter of the interior pipe. When the diameter of the interior pipe is between 70 mm and 80 mm, the transmission loss firstly increases and then decreases with the center distance of the rushing hole changes from Smin to Smax. When the diameter of the interior pipe is between 80 mm and 90 mm, the transmission loss decreases with the center distance of the rushing hole changes from Smin to Smax. The effect of the rushing hole shape on transmission loss is not significant. The transmission loss increases with the increase of the number of rushing holes, but the increase of transmission loss is not significant with the number of rushing holes changes from 4 to 6 groups. Taking the transmission loss as the optimization index, the better experimental condition was obtained. Compared to the not optimized muffler of the sample engine, the average transmission loss of the optimized muffler is increased by 48.70 % when the frequency is 0-1000 Hz, the average insertion loss of the optimized muffler is increased by 7.4 %. At inlet air velocity of 40 m/s, the pressure loss is reduced by 56.8 %.

Characterization and Hygroscopic Behavior of Mentha crispa Powder Obtained by Foam-Mat Drying

Objective: Characterize and determine the hygroscopic behavior of Mentha crispa powder by obtaining adsorption isotherms and fitting nonlinear regression mathematical models. Theoretical Framework: Mentha crispa is an economically important species in the industry that, due to its perishability, is subjected to drying to increase its shelf life. Hygroscopicity is linked to physical, chemical and microbiological stability and it is necessary to understand its behavior to enable the use of biological products in regions with different climatic characteristics. Method: The powder was characterized according to water content, yield, water solubility index, pH, soluble solids, particle size and color. The adsorption isotherms were obtained by the indirect static method at a temperature of 25 °C. The BET, linear BET, Chung and Pfost, GAB, Halsey, Henderson, Langmuir, Oswin and modified Peleg models were adjusted to the experimental data. Results and Discussion: The results obtained revealed that the increase in drying temperature caused a darkening and degradation of the green hue of the powder. The powder isotherms presented type III behavior, with a sigmoidal shape common to food products. The Oswin and Henderson models are the ones that best represent the adsorption isotherms of Mentha crispa powders under the conditions studied. Research Implications: The research contributes to the literature with practical information on the hygroscopic behavior of the material. Originality/Value: Highlights the importance of adjusting nonlinear regression models to the adsorption isotherms of biological products. The models help to predict the behavior of a material during storage, informing the water content under a given environmental condition.

Carbonate Rock Physics Model Using Different Approaches to Estimate Rock Frame Stiffness

Deepwater carbonate reservoirs in the Brazilian pre-salt have emerged as significant hydrocarbon plays in the region and globally. Seismic monitoring of these reservoirs is crucial to provide information for well placement assessments, to reduce uncertainty in reservoir models, and to enhance model-based decision making. To integrate seismic data quantitatively, a petroelastic model (PEM) is required to estimate elastic properties. The modeling of the rock frame stiffness is an essential part in the study of PEM. However, this becomes more complex for carbonate rocks given their diverse pore-types. One way for estimating stiffness is to model each distinct pore-type and include them into the overall rock frame (different inclusion models). In this research, an alternative approach is explored to estimate the rock frame stiffness. A proxy model is employed, and its coefficients are subsequently calibrated with well-log information. Two PEMs were developed using different approaches for rock frame stiffness modeling. The first was inspired by inclusion models and incorporated two pore types (compliant and stiff pores) for the modeling process. The second considered a mathematical regression model as a proxy to relate reservoir porosity to the rock frame stiffness. These two PEMs were tested on the Brazilian pre-salt carbonate rocks of the Barra Velha (BVE) formation using well-log data from three wells and core sample measurements for one well. The accuracy of the PEMs’ performances was assessed by measures (such as, mean absolute error and root mean square error) and visual comparisons of their predictions. The results showed that both PEMs predicted velocities (compressional and shear) within an acceptable match with the actual well-log data. Similarly, when tested on the core samples, the PEMs produced comparable results, which indicates the validity of the proxy, not only at the well-log scale but at the core scale. The visual comparison and the accuracy analyses of the results for all three wells confirmed that the two PEMs had comparable predictions. This is significant given that the proxy simplifies the modeling process and facilitates the representation of various pore-types in the prediction of elastic properties in carbonate rocks. Overall, the proxy for the rock frame stiffness modeling is a computationally less expensive model compared to the inclusion models which involve modeling of various pore-types. It also offers a straightforward model which enables the quantitative integration of seismic data in a multidisciplinary team of geosciences and petroleum engineers (for instance, early engagement of petroleum engineers in 3D/4D seismic history matching).

Experimental studies on the interfacial shear characteristics between joint concrete and foamed polymer in cross-river shield tunnel

The inadequate grouting performance in cross-river shield tunnels is primarily due to the insufficient bonding strength between the grouting material and the tunnel segments. In this study, a series of tests were conducted to compare foamed polymer and common grouting materials, focusing on the tangential bonding performance of the interface with tunnel segment concrete under varying humidity conditions. The applicability of polymer for treating leaks in cross-river shield tunnels was explored. The effects of polymer density, interfacial humidity, interfacial roughness, and normal stress on the shear strength of the polymer-concrete interface were investigated. A mathematical model reflecting the interface shear characteristics between polymer and concrete was established and validated. The test results have shown that, due to the fast reaction speed and high expansion rate, foamed polymer was found to be a feasible solution for addressing leakage in cross-river shield tunnels. Compared with common grouting materials, the density of foamed polymer is controllable, and the shear strength between foamed polymer and concrete segments is less affected by humid conditions. The minimum shear strength of the interface between foamed polymer and concrete is 1.2 MPa, while the maximum is 2.0 MPa. Foamed polymer can meet the needs of leakage treatment of cross-river shield tunnel. The interfacial shear strength between foamed polymer and concrete segments is directly proportional to the polymer density, interfacial roughness, and normal stress, and inversely proportional to the level of humid in the tunnel. The influence of various factors on interfacial strength is ranked as follows: polymer density > interfacial humidity > normal pressure > interfacial roughness. The residual error of linear regression mathematical model for the shear strength of interface between foamed polymer and segment concrete follows a normal distribution. The fitting results were proven to be accurate, allowing for the intuitive prediction of the quantitative relationship between shear strength and multiple influencing factors.

Optimization of the handling behaviour of an electric car by adjusting the kinematic gains of suspension

Abstract In this paper, within the scope of development of an electric car on a vehicle platform that is borned as internal combustion engine is presented. The vehicle dynamics behaviour changes due to the change of weight distribution and centre of gravity. Replacing the internal combustion engine with an electric motor, plus the addition of the battery moves the center of gravity towards the rear of the vehicle. Due to the increase in the rear axle load, the tendency of the vehicle to oversteer increases. To overcome this phenomenon, the rear suspension kinematic gains (camber, toe change due to vertical movement of the wheel) are adjusted thanks to the multi-link suspension. In the fist part of the study the platform is measured and Admas Car model is correlated depends on measured handling metrics. Subsequently, centre of gravity changes are applied to the verified Adams Car model. Also main platform handling metrics is defined as design targets and rear axle kinematic gains are defined as design parameters. A design of experiments (DOE)-based optimization study was performed via Adams®/Insight commercial software to achieve required handling metrics which satisfy design targets. Kinematic gains of the suspension system is analyzed via Adams car and sensitivity deepens on harpoints is determined. Design space for hardpoints is determined depends on mechanical design. The lowest and highest points are determined on the x, y and z axis and experiment list are designed according to DOE. Maneuvers that are for measuring the handling metrics are performed one after the other by changing the parameters according to the boundary conditions comes from DOE. A regression mathematical model is obtained and optimum parameters are calculated for the handling behaviour optimisation. Optimum parameters are applied on the Adams Car model again and handling manoeuvres are repeated. Results show that, although weight distribution changes, handling behaviours remain the same as based platform.

自动驾驶拖拉机电液耦合转向特性研究

To solve the problem of poor steering performance of existing self-driving tractors based on electro-hydraulic coupled steering systems (E-HCSS) under multiple influencing factors, the research on electro-hydraulic coupled steering characteristics of self-driving tractors was carried out in this paper. Taking the electro-hydraulic coupled steering system as the research object, the E-HCSS test bench of the tractor was built, and the influencing factors affecting the responsiveness of the steering process were obtained through theoretical analysis: hydraulic fluid temperature, oil supply pressure and driving speed. The hydraulic fluid temperature, oil supply pressure and driving speed were taken as the test factors, and the steering system response time and response error were taken as the performance indexes for the single-factor steering test and orthogonal test. By establishing the regression mathematical model between the influencing factors and the indexes, the interactive influence of the factors on the indexes was analyzed, the optimal parameter combination was obtained, and the optimization results were verified. The test results indicated that the tractor electro-hydraulic coupling steering system could achieve good steering performance under the optimal parameter combination, and the optimal parameter combination was: hydraulic oil temperature 60°C, hydraulic oil pressure 15 MPa and driving speed 8 km/h. The study's results were as follows: hydraulic oil temperature 60°C, hydraulic oil pressure 15 MPa and driving speed 8 km/h. The study's results could provide a reference for the steering control of the self-driving tractor, the design of the self-driving steering system and the optimization of the parameters.

A new two-parameter over-dispersed discrete distribution with mathematical properties, estimation, regression model and applications

This paper focuses on the derivation of a new two-parameter discrete probability distribution. The new model is derived by mixing Poisson and Loai distributions and is named “Poisson Loai Distribution”. The paper explores various mathematical properties of the new model, introducing a count-regression model based on this distribution. The parameters of the model are estimated using the maximum likelihood estimation method. A comprehensive simulation study is utilized to assess the behavior of derived estimators. The importance of the proposed distribution is confirmed through the analysis of three real datasets. It is found that the suggested distribution has the greatest match when compared to all rival distributions, and it may be a viable alternative for assessing dispersed count data.

Determination of melon seed physical parameters and calibration of discrete element simulation parameters.

To improve the accuracy of the Hami melon discrete element model, the parameters of the Hami melon seed discrete element model were calibrated by combining practical experiments and simulation tests. The basic physical parameters of Hami melon seeds were obtained through physical experiments, including triaxial size, 100-grain mass, moisture content, density, Poisson's ratio, Young's modulus, shear modulus, angle of repose, suspension speed and various contact parameters. Taking the repose angle of seed simulation as an index, the parameters of each simulation model were significantly screened by the Plackett-Burman test. The results showed that the recovery coefficient, static friction coefficient and rolling friction coefficient of Hami melon seeds had significant effects on repose angle. Based on the steepest climbing test and quadratic regression orthogonal rotation combination test, it was determined that the significant order of the influence of various contact parameters on the angle of repose was static friction coefficient, collision recovery coefficient, and rolling friction coefficient. The optimal parameter combination was obtained through the mathematical regression model between the angle of repose and various contact parameters, namely, the collision recovery coefficient of Hami melon seeds was 0.518, the static friction coefficient of Hami melon seeds was 0.585 and the rolling friction coefficient of Hami melon seeds was 0.337. Under this condition, three static seed-dropping experiments and dynamic rolling accumulation experiments were carried out. The average simulated angle of repose was 31.93°, and the relative error with the actual value was only 1.71%. The average simulated rolling accumulation angle was 51.98°, and the relative error with the actual value was only 1.92%.

Optimization of Extraction Conditions for Water-Soluble Polysaccharides from the Roots of Adenophora tetraphylla (Thunb.) Fisch. and Its Effects on Glucose Consumption on HepG2 Cells.

The root of Adenophora tetraphylla (Thunb.) Fisch. is a common Chinese materia medica and the polysaccharides which have been isolated from the plant are important active components for medicinal purposes. The objective of the current study was to optimize the extraction parameters and evaluate the glucose consumption activity for Adenophorae root polysaccharides (ARPs). The optimization of ARP extraction was evaluated with preliminary experiments and using response surface methodology (RSM). The conditions investigated were 35-45 °C extraction temperature, 20-30 (v/w) water-to-solid ratio, and 3-5 h extraction time. The antidiabetic effects of ARPs for the glucose consumption activity were evaluated in HepG2 cells. The statistical analyses of the experiments indicated that temperature, water-to-solid ratio, and extraction time significantly affected ARP yield (p < 0.01). The correlation analysis revealed that the experimental data were well-aligned with a quadratic polynomial model, as evidenced by the mathematical regression model's fit. The optimal conditions for maximum ARP yield were 45 °C extraction temperature and 28.47:1 (mL/g) water-to-solid ratio with a 4.60 h extraction time. Extracts from these conditions showed significant activity of promoting cell proliferation from 11.26% (p < 0.001) to 32.47% (p < 0.001) at a dose of 50 μg/mL to 800 μg/mL and increasing glucose consumption to 75.86% (p < 0.001) at 250 μg/mL on HepG2 cells. This study provides a sustainable alternative for the industry since it allowed simplified handling and a specific quantity of ARPs. Furthermore, ARPs might directly stimulate the glucose consumption in the liver and showed no cytotoxicity; therefore, ARPs probably could be taken as a potential natural source of antidiabetic materials.

RESEARCH ON SOFTWARE FOR ERROR PROBABILITY PREDICTION IN BUSINESS PROCESS MODELS USING LOGISTIC REGRESSION

Business process modeling allows to graphically represent organizational activities and related events. It allows to identify areas for improving the organizational performance, define requirements for software solutions, and, in general, to facilitate communication between IT and business parties within or between different organizations. Therefore, at the stage of representing the activity in the form of a model, it is necessary to understand how likely it is that errors will occur during the implementation of the depicted business process. Thus, this study aims to improve the quality of business process models by solving the problem of predicting the error probability of business process execution. In order to assign error probabilities to each business process model from the training dataset, it is proposed to use one of the complexity metrics – the coefficient of network connectivity. To predict the error probability in business process execution, it is proposed to use the simplest and most intuitive machine learning model – logistic regression. As independent variables, it is proposed to choose the basic metrics of business process modeling – the number of nodes and arcs. Thus, the algorithm for solving the task includes steps related to calculating probabilities for the training data set, preparing training and test sets, determining regression parameters, visualization, and evaluation of training results. For the software that implements the proposed approach, a client-server architecture was chosen due to its flexibility and scalability. When developing software components, the Scikit-Learn machine learning library and the Python programming language were used to build a logistic regression mathematical model. The software tool is implemented as a web application based on MySQL, the Node JS platform, and the Express JS web framework. The quality assessment results of the developed prediction model indicate the suitability of the software tool for solving the problem of predicting the error probability of business process execution.

Establishment of a Comprehensive Evaluation System for Low Nitrogen and Screening of Nitrogen-Efficient Germplasm in Peanut

Screening for nitrogen (N)-efficient germplasm to achieve high yield and high N efficiency is an important strategy to enhance the sustainability of modern agriculture. In this study, 127 peanut (Arachis hypogaea L.) germplasm resources were comprehensively evaluated by seedling hydroponics and field. At the seedling stage, with the range of low-nitrogen screening concentrations gradually narrowed through a comprehensive membership function analysis, standard normal distribution test, and variance analysis, we found that 0.15 mM N for 24 days could be the optimal condition for evaluating the N efficiency of peanuts. Through principal component analysis and correlation analysis, dry matter weight, root/shoot ratio, N content, N accumulation, N-use efficiency, and N use index were considered to be the N efficiency parameters, and a regression mathematical model was established accordingly. In the field, peanut genotypes that differ in resistance to low-nitrogen stress were evaluated by a yield nitrogen efficiency index under normal nitrogen and no nitrogen applications to verify the results at the seedling stage. Based on the multiple phenotypic analysis, N-efficient and N-inefficient peanut genotypes among germplasm were screened, and a comprehensive evaluation system was established to provide the theoretical basis for peanut breeding and cultivation techniques.

Analysis and Testing of Pre-Cut Sugarcane Seed Stalk Sawing Performance Parameters

Sugarcane is an important economic crop in tropical and subtropical regions. Presawing planting is an important method for achieving automated and precise planting with sugarcane planting machines. The sawing process is a key stage in planting management, affecting not only the germination and survival rates of sugarcane, but also reflecting the mechanical performance of sawing. To reduce the peak sawing force and enhance the sawing surface quality of sugarcane seedlings, this study utilized a central composite experimental design method. Single-factor and multi-factor experiments were conducted with a specially designed sugarcane stalk sawing experimental rig to investigate the impact of factors such as the stalk diameter feeding speed, and sawing speed on the peak sawing force and sawing surface quality. Upon being developed and validated, multivariate mathematical regression models elucidated the relationships among these factors. The experimental results showed that the order of influence of each factor on the peak sawing force was the stalk diameter, feed speed, and sawing speed, while for the sawing surface quality, the sequence was the sawing speed, stalk diameter, and feed speed. Correspondingly, the determination coefficients for the peak sawing force and sawing surface quality prediction models were 0.9708 and 0.9675. With a maximum error of 7.6% for the peak sawing force and an average relative error of 7.1%, and a maximum error of 3.5% for the sawing surface quality and an average relative error of 2.83%, the calculated results from the regression models were in good agreement with the experimental findings. This indicates that the models are capable of quickly and accurately predicting the peak sawing force and sawing surface quality of sugarcane stalks under different conditions. The research findings provide valuable insights for the development and optimization of sugarcane stalk presawing equipment and related experimental studies.

Monitoring Equipment Malfunctions in Composite Material Machining: Acoustic Emission-Based Approach for Abrasive Waterjet Cutting

This paper introduces an Acoustic Emission (AE)-based monitoring method designed for supervising the Abrasive Waterjet Cutting (AWJC) process, with a specific focus on the precision cutting of Carbon Fiber-Reinforced Polymer (CFRP). In industries dealing with complex CFRP components, like the aerospace, automotive, or medical sectors, preventing cutting system malfunctions is very important. This proposed monitoring method addresses issues such as reductions or interruptions in the abrasive flow rate, the clogging of the cutting head with abrasive particles, the wear of cutting system components, and drops in the water pressure. Mathematical regression models were developed to predict the root mean square of the AE signal. The signal characteristics are determined, considering key cutting parameters like the water pressure, abrasive mass flow rate, feed rate, and material thickness. Monitoring is conducted at both the cutting head and on the CFRP workpiece. The efficacy of the proposed monitoring method was validated through experimental tests, confirming its utility in maintaining precision and operational integrity in AWJC processes applied to CFRP materials. Integrating the proposed monitoring technique within the framework of digitalization and Industry 4.0/5.0 establishes the basis for advanced technologies such as Sensor Integration, Data Analytics and AI, Digital Twin Technology, Cloud and Edge Computing, MES and ERP Integration, and Human-Machine Interface. This integration enhances operational efficiency, quality control, and predictive maintenance in the AWJC process.

ANALYSIS OF THE LEVEL OF OCCUPATIONAL INJURIES

Problem statement. The task of the research is evaluation the dynamics of changes in the level of injuries at work depending on the main causes of the accident. Technical, organizational, psychophysiological, technogenic, social, natural and environmental causes are closely interrelated and have a significant impact on the number of victims of accidents at the industrial plants, as well as on the number of victims of accidents with fatal consequences. The purpose of the article. The assessment of changes in industrial injuries at Ukrainian enterprises during 2010−2022. The establishing connection between social and industrial factors affecting the rate of industrial accidents. The creation of correlation-regression models for statistical evaluation and analysis of the influence of factor variables on the results of injury. Methodology. The use of descriptive statistics for the analysis of the dynamics of changes in the level of industrial injuries. Carrying out a correlation analysis to establish the density of connection between factor variables and resulting features. The use of regression-variance analysis to obtain coefficients of regression mathematical models and statistical indicators that explain the probability of significance of these coefficients. Calculation of the value of the relative error of the calculation data obtained according to mathematical models to confirm their adequacy. Scientific novelty. Multiple correlation-regression models have been developed that take into account the main causes of the accident as factor variables affecting the injury rate and the rate of fatal accidents. Practical significance. Mathematical models make estimate the impact of the main causes of accidents on the level of industrial injuries. The implementation of improved methods and means of safe activity at work is an important tool for correcting the cause-and-effect relationships of industrial injuries. Conclusions. The correlation-regression models were created to analyze the level of industrial injuries in Ukraine. The numerical calculations were carried out according to these models. The average values of the relative errors of the calculated data are 1.55 % and 6.08 %, which indicates the adequacy of the developed models.

Experimental investigation and optimization of heat pipe heat exchanger performance under two distinct heat transport mediums through TOPSIS approach

This paper presents a comprehensive thermal performance analysis of the heat pipe heat exchanger by varying the input process parameters and optimizing the best condition for waste heat recovery through the TOPSIS technique. In this work, the heat pipe is constructed with a copper tube and segmented into evaporator and condenser sections carrying hot and cold fluids with a half-fill ratio. The heat pipe is oriented in 0° horizontally with the wick material made from stainless steel and works based on the capillary principle. The efficiency of the HPHE is evaluated by varying the input operational parameters, namely heat input, mass flow rate of hot fluid, inlet temperature of hot fluid, and mass flow rate of cold fluid, on response variables, specifically thermal resistance, energy balance ratio, universal heat transfer coefficient, and effectiveness. The experiment was designed for four factors at three levels as per Taguchi’s L27 orthogonal array, and the best optimising condition was determined by employing the multi-objective technique for order preference by similarity to ideal solution (TOPSIS) approach. The best input condition found was A3B1C1D3, in which the heat input was at 90 W, the mass flow rate of hot fluid was at 0.2 kg min−1, the mass flow rate of cold fluid was at 0.1 kg min−1, and the inlet temperature was at 60 °C. The effect of process parameters and their significance on output responses was analysed by executing an analysis of variance (ANOVA) statistical method. The mathematical regression model was generated, and validation was made to investigate the prediction error percentage.

Process Optimization of Pellet Manufacturing from Mixed Materials in Ultrasonic Vibration-Assisted Pelleting

Achieving carbon neutrality and alleviating the rural energy predicament are crucial aspects in rural areas, particularly in the severe cold regions of northeast China. Pellets serve as clean, renewable energy sources and are ideal alternative fuels. This study investigated the influencing factors and effects of mixed raw materials in ultrasonic vibration-assisted pelleting (UV-A pelleting). Rice straw and corn stover were mixed to produce pellets, and a central composite rotatable design (CCRD) was conducted to analyze the variables and their interactions on pellet density and durability. Mathematical regression models for pellet density and durability were established and then validated through ANOVA analysis. The results showed that all variables significantly affected the density and durability of pellets. The mixing ratio had a greater impact on pellet durability compared to density due to differences in ingredients. The optimal combination of process parameters included a mixing ratio of 25%, molding pressure of 4 MPa, pelleting time of 37 s, and ultrasonic power output at 200 W, resulting in a pellet density of 1301.18 kg/m3 with a durability reaching 94.26%. The desirability value (0.997) under these optimal conditions confirmed the validity of the models; further experiments also verified their effectiveness. The combustion of the optimized pellet was analyzed using thermogravimetric (TG) and derivative thermogravimetric (DTG) analysis in an air atmosphere. Four combustion stages and ignition temperature were provided.

Optimization of Manifold Microchannel Heat Sink Based on Design of Experiment Method

Abstract The manifold microchannel heat sink (MMCHS) shows high potential to cope with the rapidly increasing power density of electronic devices. It is of significance to search for an optimization method to comprehensively consider multiparameter effects on the performance of microchannel. Here, we have studied the temperature and flow characteristics of the MMCHS via experimental method. Then, the effect of geometric parameters including microchannel aspect ratio (α), hydraulic diameter (Dh), and manifold block numbers (Nm) on the performance of a MMCHS were studied by numerical method. Subsequently, the design of experiment (DOE) method was proposed to optimize the geometric parameters of the MMCHS. The relation of friction coefficient and heat transfer coefficient with respect to the three geometric parameters was gained by the mathematical regression model. The statistically optimized manifold microchannel achieved a 10.0% reduction in temperature rise, 59.9% enhanced in the heat transfer coefficient, and the figure of merit (FOM) of 1.6. Our work shows a novel method to consider the effect of structure parameters on the performance of the manifold microchannel and provides a novel optimization design method for the manifold microchannel.

Modelling and Optimisation of Cooling-slope Parameters of Magnesium AZ91D using Improvement Multi-Objective Jaya Approach for Predicted Feedstock Performance

The cooling-slope (CS) casting technique is one of the simple semi-solid processing (SSP) processes a foundryman uses to produce the feedstock. This study attempts to develop mathematical regression models and optimise the CS parameters process for predicting optimal feedstock performance, which utilises tensile strength and impact strength to reduce the number of experimental runs and material wastage. This study considers several parameters, including pouring temperature, pouring distance, and slanting angles for producing quality feedstock. Hence, multi-objective optimisation (MOO) techniques using computational approaches utilised alongside the caster while deciding to design are applied to help produce faster and more accurate output. The experiment was performed based on the full factorial design (FFD). Then, mathematical regression models were developed from the data obtained and implemented as an objective function equation in the MOO optimisation process. In this study, MOO named multi-objective Jaya (MOJaya) was improved in terms of hybrid MOJaya and inertia weight with archive K-Nearest Neighbor (MOiJaya-aKNN) algorithm. The proposed algorithm was improved in terms of the search process and archive selection to achieve a better feedstock performance through the CS. The study’s findings showed that the values of tensile and impact strengths from MOiJaya_aKNN are close to the experiment values. The results show that the hybrid MOJaya has improved the prediction of feedstock using optimal CS parameters.

Simulation and Optimization of Surface Roughness and Process Performance during Machining of HSS by Micro-WEDM Technology.

When machining high-speed steels (HSS) with micro-wire electrical discharge machining (micro-WEDM), high surface quality is achieved as standard. The value of the roughness parameter Ra is less than 0.2 μm. However, the problem is the performance of the electroerosion process (MRR), which is low. This problem is related to the mechanical and physical properties of the HSS in combination with the setting of the main technological parameters (MTP). The proposed solution to eliminate this problem relies on the selection of proper procedures for the determination of optimization criteria in relation to Ra and MTP, with the inclusion of properties of the machined material. The solution consisted in the identification of four significant physical (ρ, κ) and mechanical (Rm, HRC) indicators of HSS properties, on the basis of which a suitable combination of the process output parameters Ra and MRR can be determined through established mathematical regression models using simulation and optimization. In the next step, the proper values of the MTP output process parameter settings, which correspond to the optimized output parameters Ra and MRR during machining of HSS by micro-WEDM technology, were then obtained by the same approach.