Model Of Extraction Process Research Articles

Modeling and Optimization of Lipid Extraction Process from Municipal Secondary Sludge for Biodiesel Production

In the current study, the potentiality and optimization of lipid extraction from secondary sludge for biodiesel production were investigated. Four lipid extraction parameters were examined and used for process optimization and model development using Design of Experiment (DoE) method (namely methanol to hexane ratio-%, solvent to sludge ratio-ml/g, temperature- o C and extraction time-h). During the optimization process, free fatty acid (FFA) and saponifiable lipids (SLs) content of the extracted lipid were analyzed. The results revealed that, the maximum lipid extraction yield ( Y lipid ) predicted through numerically optimized conditions by the model for highest desirability (0.99) was 16.5% at methanol to hexane ratio (%) of 84%, solvent to sludge ratio (v/wt) of 45 ml/g, temperature at 90 o C for 6 hours extraction time. The extracted lipid contained a maximum amount of 31% (wt/wt) FFA, where palmatic acid was predominant. The FAMEs yield produced from ex-situ acid-catalyzed esterification/ transesterification of the methanol-hexane co-solvent extracted lipid ranged between 4.5-5% (wt/wt) based on sludge weight. Fatty acid profile of FAMEs was found to be dominated by methyl palmitate (C16:0) representing 36% of FAMEs composition, followed by palmitoleic acid methyl ester (C16:1), oleic acid methyl ester (C18:1) and stearic acid methyl ester (C18:1) representing 24%, 18% and 10% of the FFA composition respectively. PCA analysis showed that solvent to sludge ratio (ml/g) has the highest significant positive effect on FAMEs yield (p-value < 0.05) where methanol to hexane ratio (X1), temperature (X3) and extraction time (X4) were inversely correlated with FAMEs yield. The results indicated the feasibility of using secondary sludge as an alternative feedstock for biodiesel production. However, the optimized conditions for maximizing extracted lipid content should not be considered suitable for FAMEs yield as well.

Mars Colony in situ resource utilization: An integrated architecture and economics model

This paper reports on our effort to develop an ensemble of specialized models to explore the commercial potential of mining water/ice on Mars in support of a Mars Colony. This ensemble starts with a formal systems architecting framework to describe a Mars Colony and capture its artifacts' parameters and technical attributes. The resulting database is then linked to a variety of “downstream” analytic models. In particular, we integrated an extraction process (i.e., “mining”) model, a simulation of the colony's environmental control and life support infrastructure known as HabNet, and a risk-based economics model. The mining model focuses on the technologies associated with in situ resource extraction, processing, storage and handling, and delivery. This model computes the production rate as a function of the systems' technical parameters and the local Mars environment. HabNet simulates the fundamental sustainability relationships associated with establishing and maintaining the colony's population. The economics model brings together market information, investment and operating costs, along with measures of market uncertainty and Monte Carlo techniques, with the objective of determining the profitability of commercial water/ice in situ mining operations. All told, over 50 market and technical parameters can be varied in order to address “what-if” questions, including colony location.

Application of genetic algorithm on model-based optimisation of supercritical carbon dioxide extraction: An overview

Green supercritical carbon dioxide extraction technology has gained enormous interest especially in the application of extraction of natural products. The use of carbon dioxide as solvent in the extraction process is very advantageous as carbon dioxide is cheap, non-toxic, high selectivity, and can easily be separated. Supercritical carbon dioxide extraction is considered as a complex process as more factors affect the outcome of the process as compared to conventional extraction methods. This technology requires higher cost to setup and maintain the equipment. For these reasons, the whole process needs to be fully understood to ensure that the process is well optimised. Mathematical modelling is a way to explain the relationship between process variables and the outcome of the process. The optimisation of supercritical carbon dioxide extraction needs to be highly integrated to be feasible, which means that a complex mathematical model is involved. In overcoming this problem, genetic algorithm was applied in several studies. Genetic algorithm is one of the optimisation methods that is able to optimise a complex and large scale of problems, with high accuracy and practicality. This paper intends to give an overview on the application of genetic algorithm in the optimisation of mathematical modelling of supercritical carbon dioxide extraction process.

Supercritical fluid extraction of coriander seeds: Kinetics modelling and ANN optimization

The main goal of this research was mathematical modelling and numerical simulation of the supercritical fluid extraction (SFE) process of coriander (Coriandrum sativum L.) seeds. SFE was performed at different set of process parameters: pressure (100, 150 and 200bar), temperature (40, 55 and 70°C) and CO2 flow rate (0.2, 0.3 and 0.4kg/h). Both applied empirical models, derived from models developed by Brunner and Esquivel et al., adequately described SFE process. Second objective was to determine effects of investigated SFE parameters on total extraction yield. Furthermore, calculated parameters from the empirical models were used for the calculation of initial slope, which was successfully used for optimization using artificial neural network (ANN). Optimized SFE conditions for maximized initial slope, i.e. initial rate of the solubility-controlled extraction phase, were pressure of 200bar, temperature of 40°C and CO2 flow rate of 0.4kg/h.

Computational fluid dynamic (CFD) modeling of simultaneous extraction and fermentation process in a single sugar beet cossette

Abstract For simulations of flow and microbial conversion reactions, related to modeling of simultaneous extraction and fermentation process in a single sugar beet cossette a software package OpenFOAM was used. The mass transfer of the components (sucrose, glucose, fructose and ethanol) in the studied system was controlled by the convection and diffusion processes. Microbial conversion rates and yield coefficients were experimentally determined and/or estimated by mathematical simulation. Dimensions of the model sugar beet cossette (SBC) were: average length of cosettes 40.10 mm, average thickness 3.32 mm and average width 3.5 mm, and represented in the model as a square-shape cross-section mathematical simulation. Dimensions of the model sugar beet cossette (SBC) were: average length of cosettes 40.10 mm, average thickness 3.32 mm and average width 3.5 mm, and represented in the model as a square-shape cross-section used to study the mass transfer and microbial conversion rates on the scale of single sugar beet cossette in the short time scales (up to 25 s). This model can be used for simulation of extractant flow around single sugar beet cossette as well as for description of simultaneous extraction and fermentation process in the studied system.

Modeling and Simulation of Solvent Extraction Processes for Purifying Rare Earth Metals Using Saponified PC88A

Modeling and Simulation of Solvent Extraction Processes for Purifying Rare Earth Metals Using Saponified PC88A

Process Discovery from Dependence-Complete Event Logs

Process mining, especially process discovery, has been utilized to extract process models from event logs. One challenge faced by process discovery is to identify concurrency effectively. State-of-the-art approaches employ activity orders in traces to undertake process discovery and they require stringent completeness notions of event logs. Thus, they may fail to extract appropriate processes when event logs cannot meet the completeness criteria. To address this problem, we propose in this paper a novel technique which leverages activity dependences in traces. Based on the observation that activities with no dependencies can be executed in parallel, our technique is in a position to discover processes with concurrencies even if the logs fail to meet the completeness criteria. That is, our technique calls for a weaker notion of completeness. We evaluate our technique through experiments on both real-world and synthetic event logs, and the conformance checking results demonstrate the effectiveness of our technique and its relative advantages compared with state-of-the-art approaches.

Improved dynamic modeling and simulation of an industrial copper solvent extraction process

Existing dynamic models of the mixer–settler SX process assume that the mixing stage can be modeled as a single mixer, whereas in recently developed technology, there are two mixing chambers (a pump–mixer and a mixer). Moreover, existing dynamic models assume that the hydrodynamics of the aqueous and organic phases in the settler were the same, whereas they are not similar. In this study, the existing dynamic models are extended to more accurately represent the mixing and settling behavior. Firstly, the parameters of the pump–mixer model were estimated using CFD simulation for each mixer–settler unit. Then, the dynamic simulation was performed to test the simulator with practical parameters over a period of 30days of industrial operating data. Compared to existing dynamic models, better fitting was obtained due to the continual updating of parameters, the use of multiple mixer unit models (pump–mixer and mixer) as well as allowance for different hydrodynamics of organic and aqueous phases in the settler.

A model of multicomponent extraction process in the pulsating column

The model of multicomponent process of extraction in pulsating column is presented. Model analysis on adequacy to real apparatus was made. The received results have shown, that the presented model adequacy to the real nuclear fuel-reprocessing apparatus.

Winter savory: Supercritical carbon dioxide extraction and mathematical modeling of extraction process

Main objective of this work was to investigate the influence of pressure and temperature on supercritical carbon dioxide extraction of Satureja montana in terms of extraction yield and chemical composition. The most dominant compound in all investigated extracts was oxygenated monoterpene-carvacrol. The kinetics of the supercritical carbon dioxide extraction of S. montana as well as the solubility data were investigated by modelling the extraction curves using different empirical models and all models used showed similar deviation from experimental data. Hierarchical cluster analysis was employed in order to reveal possible similarities and dissimilarities among the extracts obtained at different extraction conditions.

Scalable Process Discovery Using Map-Reduce

Process discovery is an approach to extract process models from event logs. Given the distributed nature of modern information systems, event logs are likely to be distributed across different physical machines. Map-Reduce is a scalable approach for efficient computations on distributed data. In this paper we present Map-Reduce implementations of two well-known process mining algorithms to take advantage of the scalability of the Map-Reduce approach. We present the design of a series of mappers and reducers to compute the log-based ordering relations from distributed event logs. These can then be used to discover a process model. We provide experimental results that show the performance and scalability of our implementations.

Determination of formation constants of uranyl(VI) complexes with a hydrophilic SO3-Ph-BTP ligand, using liquid-liquid extraction

Abstract Complex formation between uranyl ion, UO2 2+, and a hydrophilic anionic form of SO3-Ph-BTP4- ligand, L4-, in water was studied by liquid-liquid extraction experiments performed over a range of the ligand and HNO3 concentrations in the aqueous phase, at a constant concentration of nitrate anions at 25°C . The competition for UO2 2+ ions between the lipophilic TODGA extractant and the hydrophilic L4- ligand leads to the decrease in the uranyl distribution ratios, D, with an increasing L4- concentration. The model of the solvent extraction process used accounts - apart from uranyl complexation by TODGA and SO3-Ph-BTP4- - also for uranyl complexation by nitrates and for the decrease in the concentration of the free L4- ligand in the aqueous phase, due to its protonation, bonding in the uranyl complex and the distribution between the two liquid phases. The unusually strong dependence of the D values on the acidity, found in the experiment, could hardly be explained as due to L4- protonation merely. Three hypotheses were experimentally tested, striving to interpret the data in terms of additional extraction to the organic phase of ion associates of protonated TODGA cation with either partly protonated anionic L4- ligands or anionic UO2 2+ complexes with NO3 - or L4-. None of them has been confirmed. The analysis of the results, based on the formal correction of free ligand concentrations, points to the formation of 1 : 1 and 1 : 2 uranyl - SO3-Ph-BTP complexes in the aqueous phase. The conditional formation constant of the 1:1 complex has been determined, logßL,1 = 2.95 ± 0.15.

Modeling and simulation of multicomponent solvent extraction processes to purify rare earth metals

A huge number of nonlinear equations must be solved to simulate the multicomponent solvent extraction process for purification of rare earth metals using saponified PC88A. Typical approaches to solve them have not been successful due to the large number of initial guess values and numerical problems such as poor convergence and divergence. In this paper, the number of nonlinear equations to be solved simultaneously is dramatically reduced by a proposed model simplification technique, which reduces the number of initial guess values and overcomes the numerical problems. Furthermore, a thermodynamic equilibrium analysis algorithm using the model simplification technique is proposed to calculate the equilibrium concentrations. Finally, a new multicomponent solvent extraction process simulator is developed to calculate the equilibrium concentrations for all stages of the solvent extraction process.

Parametric optimization and modeling of batch extraction process for extraction of betulinic acid from leaves of Vitex Negundo Linn

ABSTRACTExtraction of betulinic acid from leaves of Vitex Negundo Linn was carried out in a series of solvents (methanol, ethanol, isopropyl alcohol, and water). Methanol gave maximum yield of extraction and therefore was used to investigate influence of particle size, speed of agitation, solid loading, and temperature, etc., on the rate and recovery in a stirred batch reactor. Effective intra-particle diffusivity of the acid in each solvent at different temperatures was estimated using an unsteady state mass diffusion model. The diffusivity of betulinic acid through the cellulose matrix of leaves is in the range 1.7 x 10−13 to 9.23 x 10−11 m2/sec and is strongly influenced by the temperature of the extraction. The Response Surface Methodology was used to estimate optimal conditions for betulinic acid extraction.

Process mining on noisy logs — Can log sanitization help to improve performance?

Process mining techniques are designed to read process logs and extract process models from them. However, real world logs are often noisy and such logs produce bad, spaghetti-like process models. We propose a technique to sanitize noisy logs by first building a classifier on a subset of the log, and applying the classifier rules to remove noisy traces from the log. The improvement in the quality of the resulting process models is evaluated on synthetic logs from benchmark models of increasing complexity on both behavioral and structural recall and precision metrics. The results show that mined models produced from such preprocessed logs are superior on several evaluation metrics. They show better fidelity to the reference models, and are also more compact with fewer elements. A nice feature of the rule based approach is that it generalizes to any noise pattern since the nature of noise varies from one log to another. The rules can also be explained and may be further modified manually. We also give results from experiments with a real dataset.

A hybrid approach to extract business process models with high fitness and precision

Process mining (PM) aims at extracting a process model from an event log to represent the process behavior recorded in that event log. An extracted process model with high fitness and precision means it can reflect most of the process behavior recorded in the event log (fitness) and will not generate extra behavior not recorded in the event log (precision). Most of the existing PM methods, such as the genetic process mining (GPM), focused only on the achievement of high fitness, but ignore the pursuit of high precision. This research presents a hybrid PM approach that integrates the GPM, particle swarm optimization, and differential evolution to extract process models with high fitness and precision (FP values) from event logs. The results show that the proposed approach achieves improvement in extracting process models from event logs with higher FP values.

Mathematical Modeling of the Solvent Extraction of Palm Kernel Oil from Palm Kernel

The model for the solvent extraction of palm kernel oil from palm kernel was generated for the process at varying particle sizes of palm kernel, temperature of extraction, duration of extraction and mass of palm kernel respectively using Least Square Linear Equation. Petroleum ether was used as solvent to carry out the extraction in a soxhlet apparatus. The percentage oil yield was determined for every extraction carried out. The experimental results obtained showed that percentage oil yield decreases with increase in particle size and mass, but increases with increase in the temperature and duration of extraction. The characterization of the extracted oil was also done to determine its physiochemical properties, which revealed palm kernel oil as a non-drying oil. Statistical analyses of each variable studied and its corresponding oil yield was carried out followed by the modeling of the extraction process for each parameter using least square linear equation. The interpretation of the model developed revealed a model which was significant in the variations obtained from the experimental results.

Modeling of extraction process of crude polysaccharides from Basil seeds (Ocimum basilicum l.) as affected by process variables.

Basil seed (Ocimum basilicum L.) has practical amounts of gum with good functional properties. In this work, extraction of gum from Basil seed was studied. Effect of pH, temperature and water/seed ratio on the kinetic and thermodynamic parameters; entropy, enthalpy and free energy of extraction were investigated. The maximum gum yield was 17.95% at 50°C for pH=7 and water/seed ratio 30:1. In this study, the experimental data were fitted to a mathematical model of mass transfer and equations constants were obtained. The kinetic of Basil seed gum extraction was found to be a first order mass transfer model. Statistical results indicated that the model used in this study will be able to predict the gum extraction from Basil seed adequately. It also found that ΔH and ΔS were positive and ΔG was negative indicating that the extraction process was spontaneous, irreversible and endothermic. The ΔH, ΔS and ΔG values were 0.26-7.87kJ/mol, 8.12-33.2J/molK and 1.62-4.42kJ/mol, respectively.

CFD modelling of continuous protein extraction process using liquid‐solid circulating fluidized beds

AbstractA computational fluid dynamics (CFD) model was developed to describe the hydrodynamics and mass transfer in the process of continuous protein extraction from broth using a liquid‐solid circulating fluidized bed (LSCFB) ion exchange system. The CFD model took into account both the adsorption and desorption processes. The quantitative predictions for the protein concentration were compared favourably with the experimental data from a lab‐scale system. The model was used to investigate the effects of hydrodynamic and kinetic parameters on the performance of the LSCFB ion exchange system. It was demonstrated that the model is a robust tool to simulate, scale up, and optimize the LSCFB ion exchange systems for protein extraction process.

Yellow Oleander Seed Oil Extraction Modeling and Process Parameters Optimization: Performance Evaluation of Artificial Neural Network and Response Surface Methodology

The effects of sample weight, time and solvent type on YOSO yield were evaluated using ANN and RSM. The predicted optimal condition for the extraction process was found to be the same for the ANN and RSM models developed: sample weight of 20 g, time of 3 h and petroleum ether. The models predictions of YOSO yield (ANN [77.42%] and RSM [78.64%]) at optimum levels were verified experimentally (ANN [77.63%] and RSM [76.64%]). Evaluation of the models by R2 and AAD showed that the ANN model was better (R2 = 1.00, AAD = 0.61%) than the RSM model (R2 = 0.98, AAD = 3.19%) in predicting YOSO yield. Physicochemical properties of the YOSO indicated that it was nonedible and the fatty acids profile showed that the oil was highly unsaturated (76.13%). Practical Applications This study demonstrated modeling of YOSO extraction and optimization of process parameters that are involved. The performance evaluation results showed that both the ANN and RSM could be used for modeling and optimization of YOSO extraction process. Also, the characterization of the oil showed that it could serve as raw material for many chemical industries such as biodiesel production, soap, cosmetic and pharmaceutical industrials. The results from this study can be successfully scaled up to pilot scale. Also, the results could be extended to the extraction of other oilseeds.