Interaction Of Process Variables Research Articles

Response surface methodology optimization extraction of aloins from Aloe vera leaf skin by ultrasonic horn sonicator and cytotoxicity evaluation

Aloe vera leaf skin (AVLS) is a major byproduct of plant gel treatment and a promising source of bioactive compounds. The present study aims at intensifying the extraction of AVLS using an ultrasound-assisted technique. Central composite design (CCD) protocol as a subset of Response Surface Methodology (RSM) was employed to investigate the interaction of process variables on the amount of aloin A and aloin B extraction from AVLS. The optimum extraction conditions of 30 min sonication time, EtOH 80%, and 65% sonication power were obtained corresponding to maximize extraction of aloin A (4.33 mg.g−1), and aloin B (4.46 mg.g−1), and the extraction yield was 9.43%. It was revealed that the amounts of aloin A and aloin B increased as the extraction yield decreased. To evaluate the cytotoxic effect of the extract on cancer cells MTT assay was performed on A549 cells (a human lung cancer cell line). Our preliminary results indicated that the AVLS extract could inhibit the proliferation of A549 cells (IC50 =166 µg.mL−1). Moreover, compared with conventional methods, ultrasound radiation made effective cracks in the surface structures of the leaf skin. The results of the present study may have contributed to the development of extraction methods with probe-sonic applicable to extract of aloin A and B.

Oxyalkylation of Lignoboost™ Kraft Lignin with Propylene Carbonate: Design of Experiments towards Synthesis Optimization.

Oxyalkylation with propylene carbonate (PC) is a safe process to convert lignin into a reactive liquid polyol to be used in polyurethane formulations. In this study, the effect of operating conditions of oxyalkylation (temperature, time and quantify of PC) on the quality of lignin-based polyol in terms hydroxyl number (IOH) and viscosity was studied. Full factorial modeling and response surface methodology (RSM) were applied to study the effect and interaction of process variables on the IOH and viscosity of lignin-based polyols. The results revealed that the IOH is highly affected by the reaction time, while the viscosity is affected by the amount of PC. Validation experiments confirmed the model is reliable. Furthermore, RSM optimization allowed to reduce the amount of PC by about 50% and to increase the lignin content in the polyol from 12.5% to 25% (w/w) depending on the temperature and time of the process and also on the purpose of the polyol produced (i.e., application in rigid foams or adhesives).

Identifying key interactions between process variables of different material categories using mutual information-based network inference method

This paper analyzes production data from injection molding processes to identify key interactions between the process variables from different material categories using the network inference method called “bagging conservative causal core network” (BC3net). This approach is an ensemble method with mutual information that is measured between process variables to select pairs that show significant shared information. We construct networks for different time intervals and aggregate them by calculating the proportion of significant pairs of process variables (weighted edges) for each production process over time. The weighted edges of the aggregated network for each product are used in a machine learning model to optimize the network interval size (interval split) and feature selection, where edge weights are the input features and material categories are the output classification labels. The time intervals are optimized based on the classification accuracy of the machine learning model. Our analysis shows that the aggregated edge features of inferred networks can classify different material categories and identify critical features that represent interdependence in the associated process variables. We further used the “one vs. other” labels for the machine learning models to identify material-specific interactions for each material category. Additionally, we constructed an aggregated network over all samples in which the process variable interactions were steady over time. The resulting network showed modular characteristics where process variables of similar categories were grouped in the same community.

Meticulous process monitoring with multiscale convolutional feature extraction

Due to the interaction of process variables, process data is in essence graph-structured with non-Euclidean nature. Hence, learning the graph representation in a low-dimensional Euclidean space will be helpful for gaining the true insights underlying the industrial process. In this study, a meticulous process monitoring method (PM-MCF) is proposed based on multiscale convolutional feature extraction. For the proposed method, the interactions between different process variables are identified using causality inference. According to the obtained graph structure, convolutional filters are specifically designed for each process variable. In this way, the local correlation within directly related variables and their corresponding dynamic information can be effectively extracted. Besides, with the increasing of convolutional layers, more variables can be involved through the interaction relationship to explore a larger reception field. Based on the obtained feature matrices, sub-models are developed to calculate the monitoring statistics and their corresponding control limits. Finally, the decisions of all the sub-models are integrated to identify the operation status of the process system. It is noted that the proposed PM-MCF can be readily generalized to other existing methods by replacing the selected filter and the developed sub-models. The monitoring performance of the proposed method is illustrated using process data collected from a thermal power plant. Experimental results show that the proposed method can accurately detect the process anomalies using the extracted causal relationship.

Rational design of process parameters for carbon-neutral and sulfur-free motor fuel production from second-generation biomass generated syngas

The carbon-neutral fuel produced from Fischer-Tropsch synthesis using H2/CO/CO2 mixture generated from gasification of second-generation biomass could be a replacement to the conventional crude-based fuel with an additional impressive feature of utilizing the greenhouse gas efficiently. The present study focuses upon multiple-response optimisation of process parameters in low-temperature Fischer-Tropsch reaction conditions with hydrogen deficient and carbon dioxide containing syngas feed reacted over a supported bimetallic catalyst developed and reported by the same group. Herein, we performed 28 experimental runs in a laboratory-scale high-pressure fixed-bed reactor for developing a quadratic model for identifying significant variables influencing process practicality. The developed regression models were statistically validated within a 95% confidence interval. The contribution of an individual, square and second-order interaction of process variables in the Fischer-Tropsch reaction performance are identified. A highest CO consumption rate of 13.9 mmol h−1 gcat−1 with minimum 6.8% CH4 selectivity and maximum 72.3% selectivity towards the C5+ hydrocarbons is achieved with a composite desirability of 0.94. These results are experimentally validated by performing confirmatory experiments at optimal process parameters. The distillation characteristics of liquid hydrocarbon product collected at the optimal conditions show similar T-50 cut with the commercially available refinery diesel fuel. The quality and quantity of the coke deposited on catalyst during a time-on-stream run were also evaluated and found to help this reaction.

Fabrication of mixed phase calcium ferrite and zirconia nanocomposite for abatement of methyl orange dye from aqua matrix: Optimization of process parameters

In this study, mixed phase of CaFe2O4 and ZrO2 magnetic nanocomposite (CaF‐ZO‐MNC) was fabricated through facile co‐precipitation method and was explored for abatement of methyl orange (MO) dye from aqua matrix. X‐ray diffraction (XRD) pattern of the synthesized CaF‐ZO‐MNC depicted significant diffraction peaks of ZrO2 and CaFe2O4 nanoparticles ensured the crystalline nature of the material. The coexistence of ZrO2 and CaFe2O4 nanoparticles in the composite increased the BET surface area (95.32m2/g) and improves its physicochemical properties as an adsorbent. Enhanced adsorption capacity towards MO dye was found to be 370.37 mg/g from Langmuir model, which is higher than standalone nanoscale Fe, Ca, and Zr metal oxide nanoparticles. The equilibrium adsorption data were found to follow Langmuir isotherm and the adsorption process followed second order kinetic model strictly. Response surface methodology (RSM) was utilized for optimizing the experimental conditions for maximizing the MO dye removal (%) with desirability function approach. Four factors five levels central composite design was implemented for RSM study and the simultaneous interaction of process variables on dye removal efficiency was studied by 3D response surface plots. Maximum MO dye removal of 98.92% was determined with MO dye concentration of 22 mg/L, adsorbent dose of 0.54 g/L, contact time of 25 min at recation temperature of 30 °C.

A Novel Semiparametric Hidden Markov Model for Process Failure Mode Identification

The emitting distributions of a hidden Markov model (HMM) are normally constructed using the cross moments of the process variables. Similar to the mean of a univariate probability distribution, the cross moment is the most fundamental statistic of a multivariate probability distribution, which is not capable of capturing the high-order statistical features of process data. To alleviate this limitation, the high-order equivalence of the cross moment demonstrated in this paper, as the complete dependence structure, is used to construct the emitting distribution for HMM. The complete dependence structure among the process variables is modeled in a Gaussian copula. A semiparametric data transformation is also proposed to ensure the necessary conditions for using a Gaussian copula are met. The final emitting distribution is constructed as a finite mixture of the copula models. The proposed HMM is tested on two industrial studies for performance validation. Note to Practitioners —Gaussian mixture model HMM (GMM-HMM) is an efficient and easy-to-implement tool for mode identification of dynamic industrial processes. These virtues of GMM-HMM mainly come from the use of Gaussian emitting distribution, which allows closed-form derivatives to be computed for the expectation and maximization (EM) estimation of mode parameters. The main novelty of the proposed copula mixture model HMM (SPCMM-HMM) is twofold; its emitting distribution also belongs to the exponential family, thus enabling efficient estimation of parameters through an exact EM procedure; meanwhile, it is also capable of characterizing complex dependence structures among process variables. However, the proposed SPCMM-HMM has more model parameters compared with the GMM-HMM. It excels in situations where the operation of the process system being monitored is highly integrated (with complex process variable interactions) and abundance of training data samples is available. In this paper, the interactions between process variables in both case studies are complex due to the implementation of multiple closed control loops, and a large amount of training data samples can be easily generated from simulation. The performance of the SPCMM-HMM is shown to be consistently better than that of the GMM-HMM under such settings, which are fairly common in modern industrial processes. Nonetheless, if the process system being considered is only designed for a simple operation or there is a scarcity of training data samples, the GMM-HMM is still the preferred method as it is less prone to overfitting.

Semiparametric PCA and bayesian network based process fault diagnosis technique

AbstractSemiparametric Principal Component Analysis has advantages over Principal Component Analysis (PCA), as it can deal with nonlinear and non‐monotonic correlation and non‐Gaussian distribution process data. In Semiparametric PCA the distance correlation coefficient matrix is used to replace the covariance matrix, and a semi‐parametric Gaussian transformation is used to allow variables to follow multivariate Gaussian distribution. To reduce the cost of monitoring and alarm flooding, a fault diagnosis technique, which combines Semiparametric PCA and Bayesian Network (BN), is proposed here. In the first stage, Semiparametric PCA is used to find the fault in monitored variables. Considering the interaction of process variables and historical process data, a Bayesian network is developed in the second stage. Considering Semiparametric PCA outcome as evidence, the Bayesian network applies deductive and abductive reasoning to update and analysis, which assist in determining the true root cause(s) and fault propagation pathway. The implementation and applicability of the proposed methodology are demonstrated using three process systems.

Hurdle technology for minimally processed radishes: a response surface methodology approach

Response surface methodology was used to determine the optimum processing conditions that yielded maximum vitamin C content, minimum discoloration and minimum polyphenoloxidase activity after 4 days of refrigerated storage of minimally processed radishes. Ascorbic acid (0–2 %), sodium chloride (0–1 %) and immersion time (0–2 min) at 50 °C were the factors investigated related to polyphenoloxidase activity (PPO), vitamin C (VITC) and total color difference (ΔE). Experiments were conducted according to a Box-Behnken Design with three factors at three different levels. For each response, second order polynomial models were developed using multiple linear regression analysis. Analysis of variance (ANOVA) was performed to check the adequacy and accuracy of the fitted models. The response surfaces showing the interaction of process variables were constructed and analyzed. Based on desirability function, optimum operating conditions were found to be 2 % of ascorbic acid, 0 % of NaCl and 1.5 min of immersion time at 50 °C. At this optimum point, relative PPO activity, relative VITC content and ΔE values were 0.218, 3.227 and 4.929, respectively.

Optimization of Adsorption Parameters for Removal of Carbaryl Insecticide Using Neem Bark Dust by Response Surface Methodology

Adsorptive removal of carbaryl from aqueous solutions by neem bark dust (NBD) was investigated in a batch method under laboratory conditions. At first, the effects of particle size, stirring rate, and contact time on the adsorption process were studied. The optimum value of particle size, stirring rate, and contact time were 200 μm, 250 rpm, and 25 min, respectively. Subsequently, response surface methodology (RSM) was applied to investigate the effects of other operating parameters such as solution pH (2–10), adsorbent dose (0.01–1 g), and initial concentration (5–20 ppm). The optimization of the process parameters and calculation of the effects and interactions of process variables were done by using Box-Behnken design (BBD) which is a subset of RSM. The independent variables were precisely optimized by making use of an objective function called “desirability function.” Based on the adsorption capacity and economical use of adsorbent, the input parameters were optimized by setting two different sets of criteria (I and II). The desirability of two different sets were 1.00 and 0.822, respectively, which explains that the estimated function can well represent the experimental model. The optimized result revealed that the NBD can be an effective adsorbent for the removal of carbaryl from an aqueous system. The adsorption of carbaryl on NBD was best analyzed with the Langmuir isotherm and the pseudo-second order kinetic model. From the kinetic study, the maximum adsorption capacity was found to be 142.85 mg g−1. Thermodynamic data confirmed the feasibility and spontaneous nature of the adsorption process.

Effect of process variables interaction on simultaneous adsorption of phenol and 4-chlorophenol: statistical modeling and optimization using RSM

Results of the interaction of process variables and the consequential mixture of phenolic compounds adsorption study are expected to shed brighter light on the wastewater treatment applications. Accordingly, the aims of this research are to model and optimize the process variables which impinged on the simultaneous adsorption of phenol and 4-chlorophenol (4-CP) in the binary solution by spherical activated carbon (SAC). Batch assessments were designed using response surface methodology software. The process variables, namely SAC dosage and pH were varied over the 1.50–3.50 g/L and 4.00–9.00 g/L ranges, respectively, were experimented. The analysis of variance results showed the significant models could precisely predict the percentage removals of phenol and 4-CP, indicating models reliability. The interaction of process variables was inconspicuous for the case of phenol adsorption. However, increasing the pH would deteriorate the 4-CP adsorption which was partially offset by raising the SAC dosage. Considering the environmental benefits, optimization taken place at the SAC dosage and pH of 3.50 g/L and 7.60 g/L, respectively, was selected. By employing the optimized conditions of SAC dosage of 3.50 g/L at pH 7.60 for the adsorption process, the predicted phenol and 4-CP removal percentages were found to be 85.4 % (73.1 mg/g) and 96.2 % (82.6 mg/g), respectively, which were in agreement with the experimental runs.

Process parameter optimization for transformer oil extraction fromTerminalia catappa seed using response surface methodology

Optimization of the key process parameters of solvent extraction of Terminalia catappa L (TC) oil was carried out using response surface methodology (RSM). Effects of temperature, particle size and extraction time on the oil yield were evaluated. The process fitted (R2 = 96.94%) to the second-order quadratic model, while interactions of process variables were significant on the oil yield. The optimum parameters were obtained at 55 °C, 0.5 mm and 150 min for 60.45% oil yield. The color of TC oil was pale yellow with dielectric strength of 30.61 KV, which is considered acceptable in comparison with International Electrotechnical Commission standards. Free fatty acids of saturated and unsaturated oils were 42.95% and 56.1%, respectively. The physicochemical properties of oil indicated its potential for application as transformer fluid.

Arsenic removal from contaminated drinking water by electrocoagulation using hybrid Fe–Al electrodes: response surface methodology and mechanism study

This study investigated the optimization and mechanism of arsenic (As) removal by electrocoagulation (EC) using hybrid Fe–Al electrodes. Response surface methodology (RSM) was employed to evaluate the effects of different operating conditions on As removal and voltage. Central composite design was established for the optimization of the EC process and to evaluate the effects and interactions of process variables: current density, pH, aeration intensity, and operating time. Analysis of variance showed a high coefficient of determination value (R2 = 0.9269), ensuring a satisfactory adjustment of the second-order regression model with the experimental data. Under the optimum conditions of current density 0.47 A/dm2, pH 7.0, aeration intensity 0.32 L/min, and operating time 20 min, 99.94% As were removed and the minimum energy consumption was obtained. Results confirmed the validity of the optimization and the adequacy of the model. Besides, scanning electron miscroscopy/energy dispersive spectroscopy, X-ray diffraction, and Fourier transform infrared analysis demonstrated that adsorption onto iron and aluminum hydroxides/oxyhydroxides was the predominant mechanism of As removal by EC using hybrid Fe–Al electrodes.

Using a membrane reactor for the oxidative coupling of methane: simulation and optimization

An oxidative coupling of methane (OCM) is a promising process to convert methane into ethylene and ethane; however, it suffers from the relatively low selectivity and yield of ethylene at high methane conversion. In this study, a membrane reactor is applied to the OCM process in order to prevent the deep oxidation of a desirable ethylene product. The mathematical model of OCM process based on mass and energy balances coupled with detailed OCM kinetic model is employed to examine the performance of OCM membrane reactor in terms of CH4 conversion, C2 selectivity, and C2 yield. The influences of key operating parameters (i.e., temperature, methane-to-oxygen feed ratio, and methane flow rate) on the OCM reactor performance are further analyzed. The simulation results indicate that the OCM membrane reactor operated at higher operating temperature and lower methane-to-oxygen feed ratio can improve C2 production. An optimization of the OCM membrane reactor using a surface response methodology is proposed in this work to determine its optimal operating conditions. The central composite design is used to study the interaction of process variables (i.e., temperature, methane-to-oxygen feed ratio, and methane flow rate) and to find the optimum process operation to maximize the C2 products yield.

Optimization of osmotic dehydration process for Oyster mushrooms (Pleurotus sajor-caju) in sodium chloride solution using RSM

Sodium chloride (NaCl) and water transfer were quantitatively investigated during osmotic dehydration of Oyster mushrooms (Pleurotus sajor-caju) using response surface methodology with the NaCl concentration (10– 20%, w/v), solution temperature (30–60° C) immersio n time (15–240 min) and solution to fruit ratio (4:1 to 8:1) were taken as independent process variables. Experiments were conducted in a thermostatically controlled agitating incubator. For each response, second order polynomial models were developed using multiple linear regression analysis. Analysis of variance (ANOVA) was performed to check the adequacy and accuracy of the fitted models. The response surfaces and contour maps showing the interaction of process variables were constructed. Applying desirability function method, the optimum operating conditions were found to be: solution temperature – 45o C, immersion time – 53.54 min, salt concentration – 14.09% and solution to fruit ratio 6.08:1. Corresponding to these optimum values water loss, solute gain and weight reduction were 38.13, 2.1 and 36.02 (g/100 g initial mass) respectively.

Mass exchange evaluation during optimization of osmotic dehydration for Oyster mushrooms (Pleurotus sajor-caju) in salt-sugar solution

The objective of this study was to investigate the osmotic dehydration of Oyster mushrooms in salt-sugar solution at different solution concentrations, immersion times, temperatures and solution to fruit ratio to analyze the water loss, solute gain and weight reduction. Salt-sugar uptake and water transfer were quantitatively investigated during osmotic dehydration of Oyster mushrooms using response surface methodology. Experiments were conducted in a thermostatically controlled agitating incubator. With respect to water loss, solute gain and weight reduction both linear and quadratic effects of four process variables were found to be significant. For each response, second order polynomial models were developed using multiple linear regression analysis. ANOVA was performed to check the adequacy and accuracy of the fitted models. The response surfaces and contour maps showing the interaction of process variables were constructed. Applying desirability function method, the optimum operating conditions were found to be: solution temperature – 42.3° C, immersion time – 44.21 min, salt-sugar concentration – 15 %: 52.57° B and solution to fruit ratio 4.99:1. At these optimum values, water loss, solute gain and weight reduction was 41, 2.15 and 38.6 (g/100 g initial mass) respectively.

Optimization of Osmotic Dehydration of Seedless Guava (Psidium guajava L.) in Sucrose Solution using Response Surface Methodology

Abstract In this study, osmotic dehydration of seedless guava was studied through response surface methodology. Seedless guava cubes were dehydrated in sucrose solution at different concentration (30–50% w/w), temperature (30–50°C) and immersion time (15–240 min) with respect to weight reduction, solid gain and water loss. A Box–Behnken design was used to determine the optimum processing conditions that yield maximum weight reduction, water loss and minimum solid gain. The models developed for all responses were significant (p<0.05). The response surface plots were constructed to show the interaction of process variables. Optimum process conditions were found to be sucrose concentration of 33.79% w/w, temperature of 30.00°C and immersion time of 240 min through desirability function method. At these optimum points, weight reduction, solid gain and water loss were found to be 0.189 (gg−1), 0.050 (gg−1) and 0.237 (gg−1), respectively.

Enhancing trimethylolpropane esters synthesis through lipase immobilized on surface hydrophobic modified support and appropriate substrate feeding methods

Candida sp. 99–125 lipase immobilized on surface hydrophobic modified support and appropriate substrate feeding methods were used to improve the synthesis of tri-substituted trimethylolpropane (TMP) esters, which can be used as raw materials for biodegradable lubricants. The proposed novel production method is environmentally friendly. Lipase was adsorbed on surface hydrophobic silk fibers that were pretreated by amino-modified polydimethylsiloxane. A 5-level-4-factors central composite model, including reaction time, temperature, enzyme amount, and molar ratio of fatty acid to TMP, was designed to evaluate the interaction of process variables in the enzymatic esterification. The water activity was kept constant using a LiCl-saturated salt solution. Under the optimum conditions with 30% enzyme amount and substrates molar ratio 8.4 at 45°C for 47h, the total conversion of caprylic acid is 97.3% and the yield of tri-substituted TMP esters is 95.5%. The surface hydrophobic treatment resulted in less cluster water accumulated on the surface immobilized lipase, which was demonstrated by near-infrared spectra. Consequently, the optimum temperature and water tolerance of immobilized lipase were increased. Two TMP-feeding methods were used to maintain high molar ratio of fatty acid to TMP, and increase the final tri-substituted TMP esters content exceeding 85% (w/w) in reactant.

Optimization of crystal violet dye removal using novel soil-silver nanocomposite as nanoadsorbent using response surface methodology

This paper focused on the development and application of a novel method for preparation and characterization of soil coated with plant mediated synthesized AgNPs as a novel nanoadsorbent. A cost effective and environment friendly technique was developed for green synthesis of silver nanoparticles from silver nitrate solution through the leaf extract of Azadirachta indica as reducing as well as capping agent and was then used to synthesized soil–AgNP nanocomposite. Response surface methodology (RSM) was employed to investigate the effect of different operating parameters on the uptake of crystal violet (CV) using the novel nanocomposite materials A two level three factor (23) factorial central composite design (CCD) was used for the optimization of the process parameters and to evaluate the effects and interactions of process variables: initial solution pH (4.0–9.0), time requirement (30–90min), and agitation speed (80–180rpm) were studied. Multiple response optimization was applied to the experimental data to discover the optimal conditions for a set of response, simultaneously, by using a desirability function. The optimum removal efficiency of the Ag-nanocomposite adsorbent for CV adsorption was found as 99.995%.

The Optimization of Biodiesel Fuel Production from Microalgae Oil Using Response Surface Methodology

Biodiesel fuel was produced by the transesterification of microalgae oil using sodium hydroxide as a catalyst. The oil was extracted from heterotrophic cultivated algae biomass. The transesterification process was optimized using response surface methodologyto increase the yield of methyl esters. The Box–Behnken design and fractional factorial design 24-1 points were used to investigate the interaction of process variables, such as the methanol/oil molar ratio, the percentage of sodium hydroxide, the temperature, and the reaction time in the production of biodiesel fuel, and to predict the optimum process conditions for the FAME yield. Based on the results, the optimal conditions for the synthesis of biodiesel fuel were as follows: methanol/oil molar ratio, 7:1; catalyst concentration, 1.0% (by weight of algae oil); temperature, 67°C, and reaction time, 51 minutes. The yield of FAME was confirmed by gas chromatography analysis.