Response Surface Regression Model Research Articles

Soil microbial community structure and function mainly respond to indirect effects in a multifactorial climate manipulation experiment

Future climate change scenarios predict increases in surface temperature as well as atmospheric CO2 concentration. In this study we simultaneously addressed individual and combined effects of these factors on the soil microbial community structure and function. We tested linear as well as non-linear responses in a multifactorial climate manipulation experiment.After two years of climate change manipulations on a pre-Alpine managed grassland, topsoil samples were taken for analysis of functional enzyme activities, as well as microbial community structure. Besides, soil and vegetation parameters were measured to allow evaluation of direct and indirect effects.Pronounced and statistically significant spatial effects were observed on our field site for some variables. It is assumed that the history of site preparation could provide an explanation for the observed differences. Elevation of temperature or atmospheric CO2 did not induce strong shifts of soil fungal or bacterial communities. Only the inclusion of the spatial effects in the response surface regression model allowed the detection of subtle microbial responses to climate change scenarios. Mucor globulifera responded to temperature and CO2 in a pattern similar to soil water content. An increase in the relative abundance of coprophilous white rot fungi was observed upon warming, and this might be attributed to preferences of macrofauna for warmer plots. Specific extracellular enzyme activities were positively correlated with each other, especially within two groups of enzymes, which were involved in C-acquisition and in N-mining. The latter group responded positively to elevated CO2 concentrations. Chitinolytic activity increased with the relative abundance of the nematophagous and entomopathogenic ascomycete Purpureocillium lilacinum.We conclude that the indirect effects of future climate change scenarios prevail over direct effects on soil microbial community composition and function. Soil water content, nutrient pools, atmospheric CO2 and plant root identity were identified as drivers of the observed changes after removal of unintended spatial effects. Application of advanced statistical tools, which take spatial variability into account, was necessary to detect these effects. Minor changes in the fungal community occurred already after a short period of climate manipulation. More pronounced effects of elevated atmospheric CO2 concentration and surface warming on soil microbial community structure and function are expected on the longer-term, but indirect effects will most likely remain the dominant drivers.

A Computational approach in optimizing process parameters influencing the heat input and Depth of Penetration of Tungsten Inert Gas welding of Austenitic Stainless Steel(AISI 316L) using Response Surface Methodology

Austenitic stainless steel (AISI 316L) possesses high strength, and corrosion resistance finds application as a major base material for the manufacturing of boilers, chemical reactors, etc. Tungsten Inert Gas Welding (TIG) is a common process used for making joints in the fabrication industries like chemical, railway coaches, and automobile parts. In this work, Austenitic Stainless steel plates of Size 200 x 100 x 5 mm undergoes conventional TIG process. The input parameters set for the study was Welding Current, Welding Speed and Gas flow rate and the output response chosen for the study was Depth of Penetration (DOP) and Heat Input. Central Composite design, a common response surface methodology of Design Experimentation was used to conduct the welding trials. ANOVA was conducted to find a relation between the input and the output with a certainty level of 95%. A full quadratic response surface regression model was developed between Depth of Penetration (DOP), Heat Input and welding current, Welding Speed, and gas flow rate. It was found DOP there exists a linear relation for the input parameters. The Heat input model shows a quadratic relation exists between welding current, welding speed, and output response. The model accuracy was 95.29% for DOP and 99.43% for Heat input. The difference between R-sq. and R-sq. (adj) was less than 2% for both the cases which indicates the model accurately resembles the experimental parameters chosen for the study. Desired depth & heat were accomplished at an optimum current of 185 A, welding speed of 50.11 mm/min and gas flow rate of 13.1 l/min.

Bayesian set pair analysis and machine learning based ensemble surrogates for optimal multi-aquifer system remediation design

Surrogate models are often adopted to substitute computationally intensive groundwater simulation models for aquifer management due to their high effectiveness and computing efficiency. However, solutions of using only one surrogate model are prone to large prediction uncertainty. This study compares individual surrogate models and an ensemble surrogate model in an optimal groundwater remediation design problem. Three machine learning based surrogate models (response surface regression model, artificial neural network and support vector machine) were developed to replace a high-fidelity solute transport model for predicting saltwater intrusion and assisting saltwater scavenging design. An optimal Latin hypercube design was employed to generate training and testing datasets. Set pair analysis was employed to construct a more reliable ensemble surrogate and to address prediction uncertainty arising from individual surrogate models. Bayesian set pair weights were derived by utilizing full information from both training and testing data and improved typical set pair weights. The individual and ensemble surrogate models were applied to the salinization remediation problem in the Baton Rouge area, southeast Louisiana. The optimal remediation design includes two conflicting objectives: minimizing total groundwater extraction from a horizontal scavenger well while maximizing chloride concentration difference to the MCL (maximum contamination level) at monitoring locations. NSGA-II (Non-dominated Sorting Genetic Algorithm II) was employed to solve the nonlinear optimization model and obtain Pareto-optimal pumping schedules. The optimal pumping schedules from ensemble surrogate models and individual surrogate models were verified by the solute transport model. The study found that Bayesian set pair analysis builds robust ensemble surrogates and accounts for model prediction uncertainty. The ensemble-surrogate-assisted optimization model provides stable and reliable solutions while considerably alleviating computational burden.

Zn/HY-Zeolite as a Catalyst for Upgrading Iraqi Heavy Crude Oil Using Aquathermolysis Method

Upgrading heavy crude oil from East of Baghdad oilfield using a high pressure reactor with steam supplied and toluene as hydrogen donor has been investigated through aquathermolysis process. Synthesized Zn/HY Zeolite, which is characterized by XRD, SEM, EDX, FT-IR, BET-surface area and pore volume has been employed as a bi-functional catalyst. Statistical design of experiment (DOE) is also applying in determine the optimum operating conditions of aquathermolysis reaction under different temperatures, time and weight percentage of catalyst with the targeted response being the viscosity after upgrading. The extent of upgrading might be established through various analyses of the initial unconventional oil and final produced crude oil. These were included the measurements of oil density, API gravity, oil viscosity, flash point, pour point, sulfur content and yield of light and intermediate fractions. The results show that the optimum operating conditions are found at 0.25 wt% of catalyst, 231.25°C and 30 h reaction time. The experimental data under these conditions has confirmed that the viscosity and sulfur are decreased by 67.25% and 55.58% respectively, whilst the API has increased up to 24.5. The response surface regression model equation in coded unit for viscosity reduction percentage has been established.

Study on the tensile modulus of seamless fabric and tight compression finite element modeling

Tight compression garments are able to exert pressure on the surface of the human body, helping to relieve muscle fatigue and accelerate recovery. In order to study the effect of the tensile property on fabric pressure on the human body, by response surface methodology, in this paper 13 seamless knitting fabrics were designed with various knitting parameters, including the linear density of bare elastic yarn (33.3, 55.6 and 77.8 dtex), yarn feed tension (0.015, 0.030 and 0.045 cN/dtex) and fabric structure (1 × 1 mock rib, cross-float and plain stitch). Through tensile testing, the tensile moduli of 13 fabrics were measured and the effect of knitting parameters was analyzed. In addition, a finite element method was used to simulate the tight pressure on the human thigh of fabrics with different tensile moduli with ANSYS workbench 19.0. Furthermore, an actual pressure experiment was designed to prove the accuracy and validation of the simulation. The result showed that the effect of yarn feed tension on the elasticity modulus was the least significant, while linear density and structure had a great influence. A quadratic response surface regression model of the elasticity modulus was created, which could calculate a bare elastic yarn knitting fabric by using its parameters. It was proved that the finite element model was able to predict pressure accurately. Through the pressure numerical simulation of three fabric samples with different tensile moduli (0.111, 0.253 and 0.523 MPa), it was indicated that for fabric with a low tensile modulus, its tight compression merely changed as elongation increased; however, for fabric with a high tensile modulus, tight compression was promoted as elongation increased.

Modeling and optimizing the effect of extrusion processing parameters on nutritional properties of soy white flakes-based extrudates using response surface methodology

Thermomechanical effects of extrusion cooking leads to physiochemical changes that may be either beneficial or detrimental. The effects of extrusion processing on the nutritional value of food constituents, specifically protein, as well as the ultimate product are ambiguous. During the extrusion process, peptide bonds are hydrolyzed and molecular structure of protein changes, which can reduce the nutritional value of the protein and amino acids. Our objective was to study the effects of inclusion of soy white flakes (SWF, up to 500 g/kg) and other extrusion processing parameters such as moisture content of feed, barrel temperature and screw speed on the nutritional properties of aquafeed extrudates using a single-screw extruder and to optimize the processing conditions. Response surface regression models were established to correlate the nutritional properties of extrudates to the process variables. Using response surface methodology, extrusion processing was optimized for the maximum values of the analyzed nutritional properties and minimum values of trypsin inhibitor activity (TIA). The proximate composition, lysine content and TIA were significantly (P < 0.05) affected by the process variables. SWF was the most significant variable with quadratic effects on most of the properties. An increase in lysine content, TIA, protein content, ash content and decrease in fat content was observed when SWF was increased from 200 to 500 g/kg in the feed. Increasing temperature resulted in decrease in lysine content and TIA of extrudates. Our study shows that aquafeed with maximum lysine content and minimum trypsin inhibitor activity can be obtained by adding SWF up to 500 g/kg.

The optimal concentrations of Purpureocillium lilacinum and jasmonic acid in controlling Meloidogyn javanica on tomato

Management of root-knot nematodes (Meloidogyne spp.) is too difficult and is mainly based on chemicals. Synthetic nematicides contaminate the environment and endanger the human health, so scientists have been tried to find a new alternative safe method for nematode control. Activating plant immunity system in integration with biological control seems promising. Here, we tried to control Meloidogyne javanica on tomato plants by simultaneous application of jasmonic acid (JA), as a defence inducer, and Purpureocillium lilacinum (Pl) as a biocontrol agent. A factorial experiment was devised with two main factors each in four levels. The concentration of JA and Pl was 0, 0.5, 1, and 1.5 mM and 0, 103, 106, and 109 spore ml−1 suspension, respectively. Cadusafos was used as positive control. Tomato growth characteristic as well as nematode reproduction traits were evaluated 8 weeks after being grown in a greenhouse. The data were analysed by a custom response surface regression model. Increase in concentration of main factors led to increase in plant growth and decrease in nematode reproduction. JA at 1.5 mM concentration could control nematode the same as cadusafos regardless to fungus concentration. Simultaneous application of JA and Pl reciprocally increase the effect of each factor. The lowest concentration of P. lilacinum and JA for achieving the compromise best plant growth and lowest nematode reproduction were 1.5 mM JA and 40.51 × 106 conidia of P. lilacinum ml−1 suspension.

Identifying the optimal FSW process parameters for maximizing the tensile strength of friction stir welded AISI 316L butt joints

AISI 316L stainless steel sheets were friction stir welded in the butt joint configuration as per the design of experiment (DoE) developed using the response surface method; Box Benkhen design. The primary friction stir welding (FSW) process parameters; tool spindle speed, tool transverse speed, downward force and tool tilt angle were considered as factors of the experiment. A response surface (second order) regression model for the response, ultimate tensile strength (UTS) of the joints as a function of tool spindle speed, tool transverse speed, downward force and tool tilt angle was developed. Statistical tests such as ANOVA, F- ratio and values of actual and adjusted R2 were used to verify the adequacy of the developed model and the model was validated using conformity experimental trials. The analysis of the model showed that the UTS of the joint is sensitive to all the primary FSW parameters. Also, the tool spindle speed, tool traverse speed and downward force have significant interaction effect on the UTS of the joints. The non-linear optimization of the FSW process yielded 604 MPa as optimal UTS corresponding to optimal tool spindle speed of 597 rev/min, tool traverse speed of 74 mm/min, downward force of 13 kN and tool tilt of 1.5°.

Optimization of biodiesel production by microwave irradiation-assisted transesterification for waste cooking oil-Calophyllum inophyllum oil via response surface methodology

In this study, microwave irradiation-assisted alkaline-catalysed transesterification was used to produce W70CI30 biodiesel from a mixture of waste cooking oil and Calophyllum inophyllum oil. The methanol/oil ratio, catalyst concentration, stirring speed, and reaction time were optimized using response surface methodology based on the Box-Behnken experimental design in order to maximize the biodiesel yield. The quadratic response surface regression model was used to predict the biodiesel yield. It is found that the optimum methanol/oil ratio, catalyst concentration, stirring speed, and reaction time are 59.60 (v/v)%, 0.774 (w/w)%, 600 rpm, and 7.15 min, respectively, and the predicted biodiesel yield is 97.40%. Experiments were conducted using the optimum process parameters and the average biodiesel yield is 97.65%, which is in excellent agreement with the predicted value. The physicochemical properties of the W70CI30 biodiesel produced using the optimum process parameters were measured and it is found that the biodiesel has significantly higher oxidation stability (18.03 h) compared with the waste cooking oil biodiesel (4.61 h). In addition, the physicochemical properties and cold flow properties of the biodiesel fulfil the fuel specifications stipulated in the ASTM D6751 and EN 14214 standards. It can be concluded that microwave irradiation-assisted transesterification is effective to boost the biodiesel yield and produce biodiesel of superior quality. In addition, this method significantly reduces the reaction time of the transesterification process to 9.15 min and the process is energy-efficient. It is believed that the findings of this study will be beneficial for microwave irradiation-assisted biodiesel synthesis on the industrial scale.

Development of an Optimized Cassava Starch-Based Custard Powder

ABSTRACTCustard powder is a dry-formulated food product primarily made from imported corn starch in developing countries. To reduce over-dependence on corn starch, this study investigated its replacement with yellow-fleshed cassava root starch (YfCRS) in custard powder formulation. Response surface methodology was used to develop an optimized cassava starch-based custard powder based on various combinations of YfCRS (90–98%) and whole egg powder (WEP) (2–10%). The result showed that the blends of YfCRS and WEP led to custard powder with wide functional, physicochemical, chemical, and micronutrient properties, with sensorily acceptable gruel. The second order response surface regression model accurately predicted most of these quality parameters (R2 > 0.90). The optimum formulation achieved to produce an acceptable custard was 90% YfCRS and 6.07% WEP. Therefore, this study showed that YfCRS might be a very good replacement for corn starch in the production of an acceptable custard.

Experimental and numerical modelling of mechanical properties of 3D printed honeycomb structures

In recent years, 3-D printing experts have laid emphasis on designing and printing the cellular structures, since the key advantages (high strength to weight ratio, thermal and acoustical insulation properties) offered by these structures makes them highly versatile to be used in aerospace and automotive industries. In the present work, an experimental study is firstly conducted to study the effects of the design parameters (wall thickness and cell size) on the mechanical properties i.e yield strength and modulus of elasticity (stiffness) of honeycomb cellular structures printed by fused deposition modelling (FDM) process. Further, three promising numerical modelling methods based on computational intelligence (CI) such as genetic programming (GP), automated neural network search (ANS) and response surface regression (RSR) were applied and their performances were compared while formulating models for the two mechanical properties. Statistical analysis concluded that the ANS model performed the best followed by GP and RSR models. The experimental findings were validated by performing the 2-D, 3-D surface analysis on formulated models based on ANS.

Valorization of Fish Viscera for Crude Proteases Production and Its Use in Bioactive Protein Hydrolysate Preparation

Increasing environmental pollution and limited biological resources has emphasized the need for better utilization of fish processing waste. In the present study, visceral proteases from little tuna (Euthynnus affinis), catla (Catla catla) and tilapia (Oreochromis mossambicus) belonging to different habitats using acetone, ethanol and ammonium sulfate fractional precipitation were isolated and characterized. Enzymes precipitated from little tuna and tilapia exhibited higher specific activity at 40% saturation for ammonium sulfate fractional precipitation and the specific activities were 18.19 and 13.67 U/mg for little tuna and tilapia enzymes, respectively. Enzymes precipitated from catla reported higher specific activity of 8.32 U/mg at 60% saturation for ammonium sulfate fractional precipitation. Acetone precipitated fraction exhibited higher recovery for all crude enzymes studied. The effect of different combinations of crude tuna visceral waste enzyme and papain enzyme on hydrolysate properties was optimized using response surface methodology. Second order response surface regression model was used to explain the total variability in terms of quadratic and interaction effects of combination enzyme concentration on the properties of croaker protein hydrolysate with a significant R2 value for protein content (0.92), tyrosine content (0.99), degree of hydrolysis (0.99), DPPH free radical scavenging activity (0.97), ferric reducing antioxidant power (0.93), metal chelating activity (0.88), foaming capacity (0.92) and foam stability (0.88). Based on the desirability score, papain at 0.81% concentration and crude tuna visceral protease at 4.36% was found to be optimal for achieving desired properties of croaker protein hydrolysate.

Design of robust battery capacity model for electric vehicle by incorporation of uncertainties

Summary The improvement in the operating range of electric vehicles can be accomplished by robust modelling of the design and optimization of the energy storage capacity of the battery pack system. In this work, the authors have conducted a comprehensive survey on battery modelling methods and identified critical areas of improvement vital for estimating the battery capacity. This work proposes the artificial intelligence approach of automated neural networks search (ANS) in development of the robust battery capacity models for the lithium ion batteries based on the inputs (temperature and discharge rates). The robustness in the models is introduced by incorporating uncertainties in the inputs (the temperature and discharge rates, the architecture of algorithm and the models). The statistical analysis and validation of the models reveal that the models formulated using an ANS approach outperform the response surface regression models with correlation coefficient achieved as high as 0.97. The uncertainty analysis based on normal distribution of the inputs suggests that the models formulated from ANS are least sensitive to change in the input conditions when compared to response surface regression models. The global sensitivity analysis reveals that the temperature is a dominant factor for accurate battery capacity estimation. Copyright © 2017 John Wiley & Sons, Ltd.

Effects of processing conditions on the system parameters during single screw extrusion of blend containing apple pomace

AbstractThe nutritional profile of apple pomace and soy makes them ideal for development of a new extruded snack food with health benefits. Understanding how blend of apple pomace, defatted soy flour, and corn grits behave under different process conditions during extrusion will help in formulating the extrusion process. Experimental design with apple pomace level (0–20%), barrel and die temperature (100–140 °C), screw speed (100–200 rpm), and moisture content of blend (14–20% wet basis) as independent variables produced 27 different combinations that were studied using response surface methodology to investigate the effect of these variables on system parameters (apparent viscosity, mass flow rate, torque, die pressure, dough temperature, and SME). As the temperature profile increased, apparent viscosity, die pressure and specific mechanical energy decreased. Increase in apple pomace level resulted in significant (p &lt; .05) decrease in apparent viscosity, SME, and torque, and significant (p &lt; .05) increase in the mass flow rate. Response surface regression models were established to correlate the system parameters to the process variables. The data obtained from the study could be used for control of extrusion process of blends containing the aforementioned ingredients.Practical applicationsDuring extrusion process, the shearing effect of screws along with thermal energy and other extruding conditions modifies the rheological, physical and chemical properties of the raw materials. Investigating the changes in system parameters will provide better insight into understanding such modifications. In this study, we have chosen raw materials that are high in protein and fiber, and the effect of extrusion processing on the system parameters such as viscosity, SME, MFR, and torque was evaluated. The study provides new experimental data of system parameters of apple pomace‐soy flour‐corn grits dough during single screw extrusion. The results obtained will be of great significance for formulation and development of snacks using the above mentioned raw materials.

Response Surface Regression Model in Optimization of Alum Sludge Drying Facility: Solar-Fenton’s Reagent Dewatering

Response Surface Regression Model in Optimization of Alum Sludge Drying Facility: Solar-Fenton’s Reagent Dewatering

Multivariate global index and multivariate mean square error optimization of AISI 1045 end milling

Surface roughness is used as a product quality index and technical requirement of machined parts. Consequently, it is important to know the relationship between process parameters and roughness outcomes. Considering that there are different outcomes for measuring roughness, the present works aim the modelling and optimization of surface roughness based on principal component analysis using the multivariate mean square error and the multivariate global index methods. This paper presents a sequential methodology on roughness surface multivariate modelling and optimization. Initially, a complete factorial design was used with centre points, and the hardness of machined surfaces as a covariate was taken into account. On the second part, the axial points were supplied to complete the central composite design and fit a second-order model. Principal component analysis was applied to represent the set of roughness responses and fit a unique response surface regression model in terms of cutting data. The different non-linear programming methods were applied and compared through the global percentage error criteria, considering the outcomes targets. Confirmation runs on multivariate global index optimal point achieved original responses means within the confidence interval of optimal point and very near to the fitted value.

A Systematic Approach to the Comparison of Cost Efficiency of Endopeptidases for the Hydrolysis of Atlantic Salmon (Salmo salar) By-Products.

The hydrolytic and cost efficiencies of five endopeptidases (Alcalase 2.4L, Corolase 7089, Neutrase 0.8L, Promod 671L and Protex 7L) to hydrolyze Atlantic salmon by-products were compared at standardized activity levels based on a casein assay. The substrate was characterized prior to the hydrolytic experiments (pH=6.5, t=50 °C) to obtain substrate--specific constants for nitrogen to protein mass (in g) ratio, i.e. conversion factor fN=5.23 and total amount of peptide bonds (htot)=9.3 mmol per g of protein. At low enzyme activity to substrate ratio, all enzymes were equally efficient in hydrolyzing the substrate. At highest enzyme activity to substrate ratio, Protex 7L, Alcalase 2.4L and Promod 671L gave higher degree of hydrolysis (DH=14.2-14.6%) than Corolase 7089 (13.2%) and Neutrase 0.8L (11.6%) after 120 min of hydrolysis. No differences were observed in protein recovery (yield of solubilized protein) relative to DH. Determination of DH was followed by the pH-STAT and o-phthaldialdehyde methods. Based on pH-STAT data, response surface regression models were established based on the combined effects of hydrolysis time and enzyme activity to substrate ratio on DH and protein recovery. The modelling approach was combined with enzyme cost to identify the most cost-efficient enzyme (Protex 7L).

Optimum Concentrations of Trichoderma longibrachiatum and Cadusafos for Controlling Meloidogyne javanica on Zucchini Plants.

A factorial experiment was established in a completely randomized design to verify the effect of different inoculum levels of an Iranian isolate of Trichoderma longibrachiatum separately and in combination with various concentrations of cadusafos against Meloidogyne javanica in the greenhouse. Zucchini seeds were soaked for 12 hr in five densities (0, 10(5), 10(6), 10(7), and 10(8) spores/ml suspension) of the fungus prior to planting in pots containing four concentrations of cadusafos (0, 0.5, 1, and 2 mg a.i./kg soil). The data were analyzed using a custom response surface regression model and the response surface curve and contour plots were drawn. Reliability of the model was examined by comparing the result of new experimental treatments with the predicted results. The optimal levels of these two variables also were calculated. The interactive effects of concentrations of Trichoderma and cadusafos were insignificant for several responses such as the total number of eggs per gram soil, the number of intact eggs per gram soil, nematode reproduction factor, and control percent. Closeness of experimental mean values with the expected values proved the validity of the model. The optimal levels of the cadusafos concentration and Trichoderma concentration that caused the best plant growth and lowest nematode reproduction were 1.7 mg a.i./kg soil and 10(8) conidia/ml suspension, respectively.

Effect of feed moisture, extrusion temperature and screw speed on properties of soy white flakes based aquafeed: a response surface analysis.

Soy white flakes (SWF) is an intermediate product during soy bean processing. It is an untoasted inexpensive product and contains around 51% of crude protein. It can be a potential source of protein to replace fish meal for developing aquafeed. The extrusion process is versatile and is used for the development of aquafeed. Our objective was to study the effects of inclusion of SWF (up to 50%) and other extrusion processing parameters such as barrel temperature and screw speed on the properties of aquafeed extrudates using a single-screw extruder. Extrudate properties, including pellet durability index, bulk density, water absorption and solubility indices and mass flow rate, were significantly (P < 0.05) affected by the process variables. SWF was the most significant variable with quadratic effects on most of the properties. Increasing temperature and screw speed resulted in increase in durability and mass flow rate of extrudates. Response surface regression models were established to correlate the properties of extrudates to the process variables. SWF was used as an alternative protein source of fish meal. Our study shows that aquafeed with high durability, lower bulk density and lower water absorption and higher solubility indices can be obtained by adding SWF up to 40%.

Physical Properties of Refabricated Rice as Affected by Extrusion: A Response Surface Analysis

The aim of this study was to optimize the extrusion conditions for production of refabricated rice using a response surface modeling approach. The effects of feed moisture content, screw speed, and barrel temperature on the physical properties of rice grains refabricated using a corotating twin-screw extruder were investigated. Rice flour (variety PR-116) obtained from broken grains (smaller than one-eighth of a whole grain) was used in the study. Screw speeds at five levels between 49 and 150 rpm, barrel temperatures between 59 and 110°C, and feed moisture contents between 31 and 45% were tested. All of the physical properties of the refabricated rice grains that were evaluated (thousand kernel weight, length/breadth ratio, bulk density, and breaking strength) were significantly (P < 0.01) affected by these three process variables. Response surface regression models were established to correlate the physical properties of refabricated rice grains to the process variables. Understanding the effects of pro...