Quadratic Polynomial Regression Model Research Articles

Impact of China’s Digital Economy on Integrated Urban–Rural Development

This paper measures the comprehensive level of the digital economy and integrated urban–rural development across Chinese provinces from 2013 to 2022. Using a two-way fixed effects model, it explores the nonlinear relationship, regional heterogeneity, and underlying causes of how the digital economy empowers integrated urban–rural development. The study finds that: (1) The digital economy promotes integrated urban–rural development, with a quadratic polynomial regression model and threshold regressive model revealing an inverted U-shaped relationship. The turning point has not yet been reached, and the promoting relationship shows a diminishing marginal trend. (2) In terms of sub-dimensions, the strengths of the promoting effect are ranked as follows: digital infrastructure construction, industrial digitization, and digital industrialization. (3) Regionally, the digital economy’s promoting effect on integrated urban–rural development is strongest in Central China, followed by Eastern China, and weakest in Western China. The innovation and contribution of this paper lie in discovering the nonlinear impact of the digital economy on China’s integrated urban–rural development, analyzing its intrinsic driving forces and regional differences, and providing valuable references for implementing regionally differentiated development policies for the digital economy and integrated urban–rural development in various regions of China.

Spatio-Temporal Dynamics Coupling between Land Use/Cover Change and Water Quality in Dongjiang Lake Watershed Using Satellite Remote Sensing

With rapid social and economic development, land use/land cover change (LUCC) has intensified with serious impacts on water quality in the watershed. In this study, we took Dongjiang Lake watershed as the study area and obtained measured data on water quality parameters from the watershed’s water quality monitoring stations. Based on Landsat-5, Landsat-8, or Sentinel-2 remote sensing data for multiple periods per year between 1992 and 2022, the sensitive satellite bands or band combinations of each water quality parameter were determined. The Random Forest method was used to classify the land use types in the watershed into six categories, and the area proportion of each type was calculated. We established machine learning regression models and polynomial regression models with WQI as the dependent variable and the area proportion of each land use type as the independent variable. Accuracy test results showed that, among them, the quadratic cubic polynomial regression model with grassland, forest land, construction land, and unused land as its independent variables was the best model for coupling watershed water quality with LUCC. This study’s results provide a scientific basis for monitoring spatial and temporal changes in water quality caused by LUCC in the Dongjiang Lake watershed.

Utilizing Gaofen-3 InSAR time series to reveal land subsidence in Beijing (China)

ABSTRACT Various applications have been achieved by Gaofen-3 images, while the time series interferometric synthetic aperture radar (TS-InSAR) analysis was rare due to the issues with baseline instability. In this study, we revealed that the primary artefacts degrading the quality of Gaofen-3 interferograms are the long spatial baselines and inaccuracy of orbit information induced by the lack of consideration of satellite orbital tube manoeuvres. To address these challenges, we have proposed an enhanced interferometry processing method aimed at isolating the main artefacts from wrapped interferograms and enhancing the interferometry capabilities of Gaofen-3 images. It mainly includes tips on estimating precise baselines based on orbital information and fringe frequency in the wrapped interferogram and refining the residual phase further by a quadratic polynomial regression model. Subsequently, the TS-InSAR analysis is successfully implemented to acquire high-accuracy land subsidence in Beijing during 2020–2021 based on these refined interferograms. This case study demonstrates the achievement of the land subsidence analysis through TS-InSAR with Gaofen-3 images, highlighting that our proposed method could enhance the quality of Gaofen-3 interferograms.

Performance optimization and experimental analysis of angle grinder with dust collection hood

The grinding process of the angle grinder generates a large amount of dust, which present hazards to the environment and workers' health. In order to capture the grinding dust more efficiently, this paper optimizes the performance of the dust collection hood of the angle grinder by building a grinding experimental platform and using the response surface method (RSM). Using the Box-Behnken design method, the collection quantity Mtotal of all dust particles generated by grinding and the collection quantity MPM10 of PM10 are used as the main performance indicators of the dust collector. Four independent factors such as grinding wheel diameter, suction pipe diameter, mounting angle, and grinding wheel speed of the dust collection hood were optimized to arrive at the best combination of parameters. Through response surface analysis, a quadratic polynomial regression model between each influencing factor and performance indicator is established, and significance test and variance analysis are performed to verify the validity and applicability of the model. Finally, through three-dimensional response surface plots and contour plots, the interactions between each influencing factor are analyzed, and the predicted values of performance indicators under optimal experimental conditions are determined. The experimental results show that the mounting angle has the greatest impact on Mtotal, and the grinding wheel speed has the greatest impact on MPM10. The field engineering application shows that the optimized dust collection hood can effectively prevent dust from escaping. The reduction efficiency of total dust concentration in the grinding place is over 83.6 %, and the reduction efficiency of respirable dust concentration is over 90.0 %. The research results of this paper provide some reference value for the research and application in the field of dust control.

Design optimization of a recycled concrete waste-based brick through alkali activation using Box- Behnken design methodology

Construction and demolition waste (CDW) generation is a major concern for the present decade owing to improperly managed disposal and re-utilization brought on by the rapid increase in the global infrastructure segment. The present study focuses on the utilization of recycled concrete waste powder as a precursor in geopolymer to design eco-friendly bricks. Three-factor and three-levels (33) designs were considered. Molarity (M) of NaOH solution, alkaline mix ratio (AMR) and curing temperature (CT) were the independent variables and compressive strength, water absorption, flexural strength, drying shrinkage, and efflorescence as response target values. The response surface method (RSM)- Box Behnken design (BBD) was used to test and develop a quadratic polynomial regression model, which suits the accuracy of the factual data. Multi-objective response optimiser and desirability analysis were carried out to identify the optimum levels. The optimum levels were 12 M, 2.32AMR, and 50 °C CT. Analysis of variance (ANOVA) reveals the molarity of NaOH solution was a significant parameter and contributed 50–70% in property performance enhancement. In addition, Scanning Electron Microscopy (SEM)- Energy dispersive spectroscopy (EDS), X-Ray diffraction (XRD), Fourier transforms infrared spectroscopy, and Thermal gravimetric analysis (TGA) was carried out to study morphology, mineralogy, bonding types, and thermal resistance of the brick mixes. Microstructural investigation divulges the presence of zeolites and calcium-silicate-hydrate gels in the brick mixes, which makes a hybrid geopolymer and boosts the alkali activation in the recycled concrete waste mix.

Experimental study on permeability and strength characteristics of MICP-treated calcareous sand

Calcareous sand is the main fill material for island reclamation projects, but untreated calcareous sand might not be used as a reclamation fill due to its poor mechanical properties. Microbial induced calcite precipitation (MICP) was directly used to consolidate calcareous sands. One-dimensional sand column tests were conducted to identify the optimized solutions and to investigate the effects of cement solution concentration, relative density, and consolidation frequencies on the permeability and mechanical properties of MICP-treated calcareous sands. Finally, three-dimensional model tests were carried out to investigate the effective consolidation range of microbially treated calcareous sands. The results show that the MICP-treated calcareous sand shows a reduction in the permeability of the sample, while the calcium carbonate cementation and its filling effect improves the mechanical properties of the soil. The one-dimentional test results show that the effective values for cement solution concentration, relative density, and consolidation frequencies range from 0.5 mol/L to 1.5 mol/L, 30%–70%, and 5–15 times. The consolidation frequencies have the greatest influence on the permeability and strength properties of the treated calcareous sand. A quadratic polynomial regression model for permeability and strength was established through response surface analysis, and the regression model proved to be highly accurate and reliable through testing. In three-dimentional tests, the consolidation range tends to move downwards in a trapezoidal shape, showing a "big bottom and small top" pattern, with a consolidation range of approximately 34 times the diameter of the pipe. This study serves as a reference for selecting consolidation parameters for subsequent tests and applications of MICP-treated calcareous sands.

Comparison and Analysis of Several Quantitative Identification Models of Pesticide Residues Based on Quick Detection Paperboard

Pesticide residues have long been a significant aspect of food safety, which has always been a major social concern. This study presents research and analysis on the identification of pesticide residue fast detection cards based on the enzyme inhibition approach. In this study, image recognition technology is used to extract the color information RGB eigenvalues from the detection results of the quick detection card, and four regression models are established to quantitatively predict the pesticide residue concentration indicated by the quick detection card using RGB eigenvalues. The four regression models are linear regression model, quadratic polynomial regression model, exponential regression model and RBF neural network model. Through study and comparison, it has been shown that the exponential regression model is superior at predicting the pesticide residue concentration indicated by the rapid detection card. The correlation value is 0.900, and the root mean square error is 0.106. There will be no negative prediction value when the expected concentration is near to 0. This gives a novel concept and data support for the development of image recognition equipment for pesticide residue fast detection cards based on the enzyme inhibition approach.

A Regression Approach to Model Refractive Index Measurements of Novel 3D Printable Photocurable Resins for Micro-Optofluidic Applications.

In this work, a quadratic polynomial regression model was developed to aid practitioners in the determination of the refractive index value of transparent 3D printable photocurable resins usable for micro-optofluidic applications. The model was experimentally determined by correlating empirical optical transmission measurements (the dependent variable) to known refractive index values (the independent variable) of photocurable materials used in optics, thus obtaining a related regression equation. In detail, a novel, simple, and cost-effective experimental setup is proposed in this study for the first time for collecting the transmission measurements of smooth 3D printed samples (roughness ranging between 0.04 and 2 μm). The model was further used to determine the unknown refractive index value of novel photocurable resins applicable in vat photopolymerization (VP) 3D printing techniques for manufacturing micro-optofluidic (MoF) devices. In the end, this study proved how knowledge of this parameter allowed us to compare and interpret collected empirical optical data from microfluidic devices made of more traditional materials, i.e., Poly(dimethylsiloxane) (PDMS), up to novel 3D printable photocurable resins suitable for biological and biomedical applications. Thus, the developed model also provides a quick method to evaluate the suitability of novel 3D printable resins for MoF device fabrication within a well-defined range of refractive index values (1.56; 1.70).

A chemometric approach based on a full factorial design to optimize terpene acylation using natural kaolin as eco‐friendly catalyst

AbstractA statistical approach was successfully applied to optimize the acylation of l‐menthol 1 in the presence of natural kaolin as an eco‐compatible catalyst. The influence of the main variation of the operating parameters, namely solvent volume, amount of the acyl donor, temperature and the catalytic rate of kaolin, was studied. A full factorial design (FFD) was used to search for the optimum of each parameter affecting the evolution of l‐menthol 1 acylation. A quadratic polynomial regression model was applied to analyse the experimental data with a p‐confidence level <.05 (95%). Statistical analysis of the data confirmed that temperature (T), acyl donor/acceptor ratio (Qa), kaolin mass (Qc) and solvent volume (V) were the significant factors affecting the l‐menthol catalytic acylation process. The determination coefficient of the adopted model was in the order of 98%. A set of optimal reaction conditions was established, and experimental tests were performed to validate the optimal conditions; the threshold of 100% was reached. Monitoring the kinetic profile of the reaction by gas chromatography (GC) showed complete conversion after 30 min of reaction. An upgrade of this developed environmentally benign process was applied to some monoterpenic alcohols, and the corresponding acetates were yielding at 45%–90%.

Optimization of Lactic Acid Fermentation from Overriped Plantain Using Response Surface Methodology (RSM)

Lactic acid is a building block chemical used by many manufacturing industries. The search for cheap biochemical feedstock and process is of great concern to lactic acid producing industries. Plantain is a rich source of carbohydrate but the overriped plantains are thrown away as waste because of their non-firmness. This study evaluates the utilization of overripe plantain as biochemical feedstock for optimum production of lactic acid. Optimization of lactic acid production with overriped plantain was studied using Response Surface Methodology. The initial pH and reducing sugar content of the plantain hydrolysate were 4.89 and 166.05 g/l respectively. The response of lactic acid concentration to four factors: substrate concentration (138.25 – 166.05 g/L), initial pH (4 – 8), fermentation temperature (30 – 50°C) and time (24 – 168 h) was studied. The lactic acid concentration ranged from 123.50 – 163.00 g/l. A statistically significant [(<I>P</I>_model><I>F</I>) < 0.0001] second order quadratic polynomial regression model was obtained for lactic acid production; the <I>R</I>² and adjusted-<I>R</I>² were 0.9935 and 0.9896 respectively. Numerical optimization was used to obtain optimum lactic acid production (157.53 g/ L) at glucose concentration, pH, temperature and time of 159.10 g/l, 7.0, 42.3°C and 60 h respectively. Overriped plantain was a good biochemical feedstock for lactic acid production.

Performance Prediction Model for Hydrodynamically Lubricated Tilting Pad Thrust Bearings Operating under Incomplete Oil Film with the Combination of Numerical and Machine-Learning Techniques

Pivoted pad thrust bearings are common machine elements used in rotating mechanisms in order to support axial loads. The hydrodynamic lubrication of such bearings has been a major subject of many investigations over the years. However, the majority of these investigations are based on full film lubrication models, when, in fact, incomplete oil film profiles appear during various operating conditions, such as startups and shutdowns. The lack of lubricant during operations can have severe impact on the bearing’s performance, affecting its ability to carry the applied axial load. The scope of the current investigation is to combine numerical analysis and machine-learning techniques in order to create a model that predicts the thrust bearing’s performance in terms of the pad’s load-carrying capacity. For this purpose, the 2-D Reynolds equation is solved numerically for a variety of angular velocities and three different lubricants: SAE 20, SAE 30 and SAE 10W40. The position of the lack of lubricant within the oil film’s control volume is studied and evaluated, together with the percentage of oil film coverage in the inlet of the pad. The results of the numerical analysis are used as input, in order to train and evaluate three different machine-learning models: Quadratic Polynomial Regression, Quadratic SVM Regression and Regression Trees. The results showed that the position of the film incompleteness affects the ability of the bearing to carry the axial load. At the same time as less lubricant entered the domain, the pressure drop could reach lower values, up to 93%. From the studied lubricants, SAE 10W40 was the one that showed the best performance results during incomplete oil film operation. Finally, the Quadratic Polynomial Regression model showed the best fit and 99% accuracy in predicting the pad’s load-carrying capacity.

Response surface analyses of antihypertensive effects of angiotensin receptor blockers and amlodipine or hydrochlorothiazide combination therapy in patients with essential hypertension.

While previous studies have examined the dose-response characteristics of certain antihypertensive drugs alone or in combination, response surface analysis for combination therapies involving angiotensin receptor blockers (ARBs) and either amlodipine (AML) or hydrochlorothiazide (HCT) has not been explored, particularly in the context of low-dose combinations. The objectives of present study were to generate useful dose-response information for the combination of ARB/AML or ARB/HCT and to predict the blood pressure lowering effects of combination therapies compared to monotherapies. We reviewed the New Drug Application data of combination drugs of ARB/AML and ARB/HCT. Data on systolic blood pressure (SBP), from studies conducted using a factorial dose-response design over a period of 8-12 weeks, were used. The placebo-subtracted SBP change was used for analysis. Response surface analyses of the collected data were conducted using a polynomial regression model. For ARB/AML combination, the quadratic polynomial regression model containing two linear terms, two quadratic terms, and one interaction term was best fitted to the naïve pooled data. Meanwhile, for ARB/HCT combination, the best-fitted model was a quadratic model that included two linear terms and two quadratic terms. The 1/2-dose combination of these medications, compared to each monotherapy, resulted in predicted SBP reductions that were 8-30% greater. The ratio of the estimated antihypertensive effects of the combination to the expected additive effects of each component ranged from 82% to 100% of the expected effect. These results can provide a rationale for developing lower-dose combinations of ARB/AML or ARB/HCT and assist in designing clinical trials.

Full factorial optimization of α-aminophosphonates synthesis using diphenylphosphinic acid as efficient organocatalyst

Diphenylphosphinic acid was used as an efficient and simple catalyst for the synthesis of the α-aminophosphonates by multicomponent Kabachnik-Fields reaction in one pot of aromatic aldehyde, aniline and diethylphosphite. Three physicochemical factors including catalyst amount, reaction time and medium temperature were optimized using a full factorial experiment design (FFD). Additionally, a quadratic polynomial regression model was applied for the analysis of the experimental data at a confidence level of 95% with p-values < 0.05. The high signification effect of the reaction time and the medium temperature on the α-aminophosphonates synthesis were confirmed by the statistical analysis. Besides, the diphenylphosphinic acid amount showed an effect on the reaction yield. ANOVA exhibited that the coefficient determination of this model up to 99.25%. This eco-friendly procedure was extended for the preparation of series of the α-aminophosphonates in ethanol as green solvent, giving the desired products with high chemical yields up to 90%.

Treatment of Coking Wastewater by α-MnO2/Peroxymonosulfate Process via Direct Electron Transfer Mechanism

Side reactions between free radicals and impurities decelerate the catalytic degradation of organic contaminants from coking wastewater by Advanced Oxidation Processes (AOPs). Herein, we report the disposal of coking wastewater by α-MnO2/PMS process via a direct electron transfer mechanism in this study. By the removal assays of the target compound of phenol, the PMS mediated electron transfer mechanism was identified as the dominated one. Water quality parameters including initial pH, common anions and natural organic matters demonstrated limited influences on phenol degradation. Afterwards, α-MnO2/PMS process was applied on the disposal of coking wastewater. The treatment not only eliminated organic contaminants with COD removal of 73.8% but also enhanced BOD5/COD from 0.172 to 0.419, within 180 min of reaction under conditions of 50 g/L α-MnO2, 50 mM PMS and pH0 7.0. COD removal decreased only 1.1% after five-time cycle application, suggesting a good reuse performance. A quadratic polynomial regression model was further built to optimize the reaction conditions. By the model, the dosage of α-MnO2 was identified as the most important parameters to enhance the performance. The optimal reaction conditions were calculated as 50 g/L α-MnO2, 50 mM PMS and pH0 6.5, under which COD removal of 74.6% was predicted. All aforementioned results suggested that the α-MnO2/PMS process is a promising catalytic oxidation technology for the disposal of coking wastewater with good practical potentials.

Modeling and optimization of mechanical properties of drilling sealing materials based on response surface method

To improve the sealing effect of a borehole and the efficiency of gas extraction, an ideal ratio of modified materials is needed to provide the optimal strength effect of the borehole sealing material. In this study, the influence of fly ash (Fa), nanosilica (nanoSiO2), and triethanolamine (TEA) on the compressive strength of cement was analyzed using a single-factor experiment. A quadratic polynomial regression model of yield stress was developed using the response surface method (RSM); the influence on the material strength based on the interaction among the three modified materials was analyzed. The optimal ratio of the three components was determined to be 5.8% Fa, 3.6% nanoSiO2, and 0.044% TEA using RSM; from this, the predicted 28 d strength was calculated to be 57.26 MPa. The experimental 28 d strength of the optimized material was determined to be 55.44 MPa; this produced an experimental error of 3.2%. The experimental strength of the optimized material was 41.14% higher than that of the blank group. In the optimized material, the Ca(OH)2 content was reduced by 0.755%, and the needle-rod ettringite (AFt) and calcium silicate hydrate (C–S–H) contents were significantly increased. The microscopic morphology was flatter and denser. This indicated that the synergistic effect of the three modified materials optimized the hydrate fraction and dense material structure; this caused an improvement in the compressive strength of the material. This work has substantial application in the development of new drilling sealing materials that promote efficient extraction and utilization of coal mine gas.

A novel composite vegetation index including solar-induced chlorophyll fluorescence for seedling rapeseed net photosynthesis rate retrieval

Net photosynthesis rate (Pn) can be used to characterize the health status of plants and their ability to accumulate organic matter. In this study, remotely sensed vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) were retrieved to build regression models to estimate rapeseed canopy Pn. Multi-source unmanned aerial vehicle (UAV) remote sensing data collected from seedling stage rapeseed were used in this study. The results showed that Pn was significantly related to traditional VIs and SIF (R2 = 0.52, p < 0.01). A quadratic polynomial regression model built using the normalized difference vegetation index performed the best on the inversion of Pn (R2 = 0.63, RMSE = 2.56, NRMSE = 0.18). Moreover, this study coupled SIF with traditional VIs by mathematical operations. The composite indices obtained by multiplication resulted in increased correlations. The inversion model established using SIF × VARI (visible atmospherically resistant index) achieved the best overall performance with 0.14 increase in R2 (0.54–0.68) and 0.48 decrease in RMSE (2.87–2.39) compared to SIF, 0.13 increase in R2 (0.55–0.68) and 0.45 decrease in RMSE (2.84–2.39) compared to VARI. Therefore, a novel composite index obtained from the multiplication operation of individual indices improved Pn retrieval of seedling rapeseed from remotely sensed UAV data. The results from this study indicate that the novel composite index has the potential for improving the accuracy of growth status monitoring compared with traditional indices.

Implementation of regression algorithms for oil recovery prediction

This paper presents the work of predicting oil production using machine learning methods. As a machine learning method, a multiple linear regression algorithm with polynomial properties was implemented. Regression algorithms are suitable and workable methods for predicting oil production based on a data-driven approach. The synthetic dataset was obtained using the Buckley-Leverett mathematical model, which is used to calculate hydrodynamics and determine the saturation distribution in oil production problems. Various combinations of parameters of the oil production problem were chosen, where porosity, viscosity of the oil phase and absolute permeability of the rock were taken as input parameters for machine learning. And the value of the oil recovery factor was chosen as the output parameter. More than 400 thousand synthetic data were used to test multiple regression algorithms. To estimate the quality of regression algorithms, the mean square error metrics and the coefficient of determination were used. It was found that linear regression does not cover all patterns in the data due to underfitting. Various degrees of polynomial regression were deployed and tested, and it was also found that for our synthetic data, the quadratic polynomial model trains quite well and perfectly predicts the value of the oil recovery factor. To solve the overfitting problem, L1 regularization known as the Lasso regression method was applied. For the quadratic polynomial regression model, the coefficient of determination was 0.96, which is a pretty good result for the test data. Thus, it is assumed that the data-driven machine learning methods discussed in the paper can be useful for predicting the oil recovery factor using practical data from oil fields at the stages of production

Broiler meat production as affected by dietary supplemental hydroxy-selenomethionine

Selenium (Se) is essential and usually deficient in corn-soy feeds for broiler chickens. Hydroxy-selenomethionine (OH-SeMet) is a commercial organic source that has not been thoroughly evaluated as a single supplemental source of Se. The objective of this study was to evaluate the increasing dietary Se supplementation from OH-SeMet on broiler growth performance, meat yields, wooden breast (WB) and glutathione peroxidase responses in tissues. A total of 1,500 Cobb vs. Cobb 500 slow feathering one-day-old male chicks were allocated to five treatments with 12 replicates of 25 broilers each in a 3-phase feeding program from d 1 to 42 (0.0, 0.15, 0.30, 0.45 and 0.60 mg/kg of Se supplemented from OH-SeMet). Supplemental Se levels were chosen in order to provide a robust dose-response adjustment and were based on known deficient and excessive dietary contents for broiler chickens. The non-supplemented starter, grower, and finisher feeds had analyzed 0.03, 0.03, and 0.02 mg/kg Se, respectively. At 35 and 42 d of age, five broilers per pen were processed for meat evaluation and scored for WB. Samples from blood, liver, jejunum, and ileum as well as breast meat samples were taken at 42 d for evaluation of glutathione peroxidase (GSH-Px) activity and lipid oxidation (TBARS), whereas breast meat was also analyzed for cooking loss (CL) and water-holding capacity (WHC). Statistical analysis was conducted using the quadratic polynomial regression model. Increasing Se from OH-SeMet led to quadratic increases (P < 0.05) in BWG from d 1 to 42, with optimal concentration at 0.47 mg/kg Se as well as optimized estimations for FCR at 0.42 mg/kg Se. Dietary Se that maximized carcass and breast meat yields at 42 d were 0.41 and 0.48 mg/kg Se, respectively (P < 0.05). Dietary Se that maximized GSH-Px activity in erythrocytes was at 0.27 mg/kg (P < 0.05). No effects of Se were observed on WB scores at 35 and 42 d of age, CL and WHC as well as on TBARS in intestine, liver and breast muscle samples (P > 0.05). Supplementation of Se from OH-SeMet in corn-soy feeds improved broiler performance, carcass and breast yields. Average Se that optimized BWG was 0.48 mg/kg, whereas 0.46 mg/kg was needed for carcass and 0.40 mg/kg for breast yield. These are total Se contents acceptable in the European Union and frequently below commercially used contents in countries that do not have a limit on minerals in feeds.

Study on optimal adsorption conditions of norfloxacin in water based on response surface methodology

Abstract The waste pomelo peel was pyrolyzed at 400 °C to prepare biochar and used as adsorbent to remove norfloxacin (NOR) from simulated wastewater. The adsorption conditions of norfloxacin by biochar were optimized by response surface methodology (RSM). On the basis of single-factor experiment, the adsorption conditions of biochar dosage, solution pH and reaction temperature were optimized by Box-Behnken Design (BBD), and the quadratic polynomial regression model of response value Y1 (NOR removal efficiency) and Y2 (NOR adsorption capacity) were obtained respectively. The results show that the two models are reasonable and reliable. The influence of single factor was as follows: solution pH &amp;gt; biochar dosage &amp;gt; reaction temperature. The interaction between biochar dosage and solution pH was very significant. The optimal adsorption conditions after optimization were as follows: biochar dosage = 0.5 g/L, solution pH = 3, and reaction temperature = 45 °C. The Y1 and Y2 obtained in the verification experiment were 75.68% and 3.0272 mg/g, respectively, which were only 2.38% and 0.0242 mg/g different from the theoretical predicted values of the model. Therefore, the theoretical model constructed by response surface methodology can be used to optimize the adsorption conditions of norfloxacin in water.

Experimental and thermal investigation on die-sinking EDM using FEM and multi-objective optimization using WOA-CS

This article proposes a numerical and experimental analysis of the effect of a die sinking electrical discharge machine (EDM) on a Nimonic C-263 workpiece with a copper-tungsten (Cu-w) electrode tool. Primarily, the finite element method (FEM) is employed to establish a two-dimensional (2-D) axisymmetric thermal model that utilizes several significant characteristics such as Gaussian heat distribution; distribution quantity of heat through the dielectric fluid, workpiece and tool; pulse on time (TON) and flushing efficacy. The model predicts the distribution profile of temperature on the workpiece during the process of die sinking EDM and obtains the material removal rate (MRR) from the temperature isotherm. An experimental analysis is performed to assess the accuracy of the results obtained from the numerical analysis, and the theoretically calculated MRR (T-MRR) is compared with the experimentally obtained MRR (E-MRR) for the effect of input process arguments such as gap voltage (VG), peak current (I), and TON. In addition, a quadratic polynomial regression model is proposed for output responses and the accuracy is checked by means of variance analysis. However, selecting the optimal combination of input process arguments is quite challenging and essential to being able to enhance the performance of the machining process. Thus, a multi-objective optimization using hybrid whale optimization with the cuckoo search algorithm (named as WOA-CS) is also proposed to find an enhanced machining performance with higher MRR values, a lower tool wear rate (TWR), and a lower surface roughness (SR). Finally, validation of these obtained optimal values is carried out with respect to the experimental results in order to prove the significance of the proposed WOA-CS algorithm in terms of relative error.