A comprehensive economic optimization methodology of divided wall columns for biopolyol separation.
ABSTRACT: Global energetic and environmental crises have attracted worldwide attention in recent years. Biomass is an important direction of development for limiting greenhouse gas emissions and replacing fossil fuel. As downstream products of biomass, some industrially valuable polyols are costly to separate via conventional distillation due to their near volatility. The use of fully heat-integrated divided wall columns (DWCs), which carry energy and equipment investment savings, is a promising technique for purifying biopolyol products. However, the design of DWCs is complex because of the greater freedom of units, so the optimization of all variables is essential to minimize the cost of separation. A response surface methodology (RSM)-based Box-Behnken design (BBD) was proposed and applied to study the interactions between groups of factors and the effects of variables on total annual cost (TAC) savings. The optimization of global variables with RSM was confirmed to be a powerful and reliable method, and the TAC savings reached 41.09% compared to conventional distillation. In short, efficient design, lower costs and energy savings for polyol separation will promote the wide application of environmentally friendly biopolyol.
Project description:Nine hundred data were collected from rigorously simulated EHIDiC for propylene/propane separation using the Aspen Plus-Matlab communication platform. Statistical analysis was performed to give a further understanding of EHIDiC, a highly coupled system. The input variables, integrated stages, and the corresponding heat of two couples of external exchangers were generated stochastically in a wide range to cover the completely reasonable operation window. The results of blocks and streams in the flowsheet were calculated as the output data, including the flow rate, the temperature, and the pressure, as well as Total Annualized Cost (TAC). The data can be reused to design and optimize both the steady and dynamic schemes of EHIDiC, especially with machine learning methods. This manuscript gave a full description of collecting and analyzing of the data used in the research article "Data-driven analysis and optimization of externally heat-integrated distillation columns (EHIDiC)" .
Project description:Carotenoids are natural pigments with substantial applications in nutraceutical, pharmaceutical, and food industries. In this study, optimization of the fermentation process for enhancement of ?-carotene and biomass production by Exiguobacterium acetylicum S01 was achieved by employing statistical designs including the Placket-Burman design (PBD) and response surface methodology (RSM). Among the seven variables investigated by two levels in PBD, glucose, peptone, pH and temperature were indicated as crucial variables (p < 0.0001) for ?-carotene and biomass productivity. Response surface methodology was further applied to evaluate the optimal concentrations of these four variables for maximum ?-carotene and biomass productivity. The optimized medium contained glucose 1.4 g/L, peptone 26.5 g/L, pH 8.5, and temperature 30 °C, respectively. A significant increase in ?-carotene (40.32 ± 2.55 mg/L) and biomass (2.19 ± 0.10 g/L) productivities in E. acetylicum S01 were achieved by using RSM, which was 3.47-fold and 2.36-fold higher in the optimized medium compared to the un-optimized medium. Further, the optimum fermentation condition in the 5-L bioreactor was achieved a maximal ?-carotene yield of 107.22 ± 5.78 mg/L within 96 h. Moreover, the expression levels of carotenoid biosynthetic genes (phytoene desaturase (CrtI) and phytoene synthase (CrtB)) were up-regulated (2.89-fold and 3.71-fold) in E. acetylicum under the optimized medium conditions. Overall, these results suggest that E. acetylicum S01 can be used as a promising microorganism for the commercial production of ?-carotene.
Project description:For all industrial processes, modelling, optimisation and control are the keys to enhance productivity and ensure product quality. In the current study, the optimization of process parameters for improving the conversion of isoeugenol to vanillin by Psychrobacter sp. CSW4 was investigated by means of Taguchi approach and Box-Behnken statistical design under resting cell conditions. Taguchi design was employed for screening the significant variables in the bioconversion medium. Sequentially, Box-Behnken design experiments under Response Surface Methodology (RSM) was used for further optimization. Four factors (isoeugenol, NaCl, biomass and tween 80 initial concentrations), which have significant effects on vanillin yield, were selected from ten variables by Taguchi experimental design. With the regression coefficient analysis in the Box-Behnken design, a relationship between vanillin production and four significant variables was obtained, and the optimum levels of the four variables were as follows: initial isoeugenol concentration 6.5 g/L, initial tween 80 concentration 0.89 g/L, initial NaCl concentration 113.2 g/L and initial biomass concentration 6.27 g/L. Under these optimized conditions, the maximum predicted concentration of vanillin was 2.25 g/L. These optimized values of the factors were validated in a triplicate shaking flask study and an average of 2.19 g/L for vanillin, which corresponded to a molar yield 36.3%, after a 24 h bioconversion was obtained. The present work is the first one reporting the application of Taguchi design and Response surface methodology for optimizing bioconversion of isoeugenol into vanillin under resting cell conditions.
Project description:Magnetite nanocrystal clusters are being investigated for their potential applications in catalysis, magnetic separation, and drug delivery. Controlling their size and size distribution is of paramount importance and often requires tedious trial-and-error experimentation to determine the optimal conditions necessary to synthesize clusters with the desired properties. In this work, magnetite nanocrystal clusters were prepared via a one-pot solvothermal reaction, starting from an available protocol. In order to optimize the experimental factors controlling their synthesis, response surface methodology (RSM) was used. The size of nanocrystal clusters can be varied by changing the amount of stabilizer (tribasic sodium citrate) and the solvent ratio (diethylene glycol/ethylene glycol). Tuning the experimental conditions during the optimization process is often limited to changing one factor at a time, while the experimental design allows for variation of the factors' levels simultaneously. The efficiency of the design to achieve maximum refinement for the independent variables (stabilizer amount, diethylene glycol/ethylene glycol (DEG/EG) ratio) towards the best conditions for spherical magnetite nanocrystal clusters with desirable size (measured by scanning electron microscopy and dynamic light scattering) and narrow size distribution as responses were proven and tested. The optimization procedure based on the RSM was then used in reverse mode to determine the factors from the knowledge of the response to predict the optimal synthesis conditions required to obtain a good size and size distribution. The RSM model was validated using a plethora of statistical methods. The design can facilitate the optimization procedure by overcoming the trial-and-error process with a systematic model-guided approach.
Project description:Talaromyces albobiverticillius 30548 is a marine-derived pigment producing filamentous fungus, isolated from the La Réunion island, in the Indian Ocean. The objective of this study was to examine and optimize the submerged fermentation (SmF) process parameters such as initial pH (4-9), temperature (21-27 °C), agitation speed (100-200 rpm), and fermentation time (0-336 h), for maximum production of pigments (orange and red) and biomass, using the Box-Behnken Experimental Design and Response Surface Modeling (BBED and RSM). This methodology allowed consideration of multifactorial interactions between a set of parameters. Experiments were carried out based on the BBED using 250 mL shake flasks, with a 100 mL working volume of potato dextrose broth (PDB). From the experimental data, mathematical models were developed to predict the pigments and biomass yields. The individual and interactive effects of the process variables on the responses were also investigated (RSM). The optimal conditions for maximum production of pigments and biomass were derived by the numerical optimization method, as follows-initial pH of 6.4, temperature of 24 °C, agitation speed of 164 rpm, and fermentation time of 149 h, respectively.
Project description:Exopolysaccharides (EPS) of fungal origin have attracted special attention from researchers due to their multifarious applications in the food and pharmaceutical industries. In the present study, optimization of the process parameters for the production of exopolysaccharide by Schizophyllum commune AGMJ-1 was studied using one factor at a time (OFAT) method, Plackett-Burman design (PBD) and response surface methodology (RSM). OFAT method revealed xylose and yeast extract to be the most effective carbon and nitrogen sources and pH 5.3 as an optimum for maximum EPS production. Xylose, yeast extract and KCl were screened as statistically significant variables for EPS production using PBD. RSM based on the central composite design estimated that maximum EPS (4.26 g L-1), mycelial biomass (14 g L-1) and specific yield (0.45 g g-1) were obtained when concentration of xylose, yeast extract and KCl were set at 2.5 g % (w/v), 0.83 g % (w/v) and 6.53 mg % (w/v), respectively, in the production medium.
Project description:Parthenium sp. is a noxious weed which threatens the environment and biodiversity due to its rapid invasion. This lignocellulosic weed was investigated for its potential in biofuel production by subjecting it to mild alkali pretreatment followed by enzymatic saccharification which resulted in significant amount of fermentable sugar yield (76.6%). Optimization of enzymatic hydrolysis variables such as temperature, pH, enzyme, and substrate loading was carried out using central composite design (CCD) in response to surface methodology (RSM) to achieve the maximum saccharification yield. Data obtained from RSM was validated using ANOVA. After the optimization process, a model was proposed with predicted value of 80.08% saccharification yield under optimum conditions which was confirmed by the experimental value of 85.80%. This illustrated a good agreement between predicted and experimental response (saccharification yield). The saccharification yield was enhanced by enzyme loading and reduced by temperature and substrate loading. This study reveals that under optimized condition, sugar yield was significantly increased which was higher than earlier reports and promises the use of Parthenium sp. biomass as a feedstock for bioethanol production.
Project description:The individual and interactive effects of three independent variables i.e. carbon source (glucose), nitrogen source (sodium nitrate) and inducer (?-caprolactam) on nitrilase production from Fusarium proliferatum were investigated using design of experiments (DOE) methodology. Response surface methodology (RSM) was followed to generate the process model and to obtain the optimal conditions for maximum nitrilase production. Based on central composite design (CCD) a quadratic model was found to fit the experimental data (p<0.0001) and maximum activity of 59.0U/g biomass was predicted at glucose concentration (53.22 g/l), sodium nitrate (2.31 g/l) and ?-caprolactam (3.58 g/l). Validation experiments were carried out under the optimized conditions for verification of the model. The nitrilase activity of 58.3U/g biomass obtained experimentally correlated to the predicted activity which proves the authenticity of the model. Overall 2.24 fold increase in nitrilase activity was achieved as compared to the activity before optimization (26U/g biomass).
Project description:The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box-Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.
Project description:The biodiesel production from waste soybean oil (using NaOH and KOH catalysts independently) was investigated in this study. The use of optimization tools (artificial neural network, ANN, and response surface methodology, RSM) for the modelling of the relationship between biodiesel yield and process parameters was carried out. The variables employed in the experimental design of biodiesel yields were methanol-oil mole ratio (6 - 12), catalyst concentration (0.7 - 1.7 wt/wt%), reaction temperature (48 - 62°C) and reaction time (50 - 90 min). Also, the usefulness of both the RSM and ANN tools in the accurate prediction of the regression models were revealed, with values of R-sq being 0.93 and 0.98 for RSM and ANN respectively.