Model Of Extraction Process Research Articles

Ultrasound-assisted dieckol extraction from Eisenia bicyclis and its evaluation as a functional ingredient for bacterial cellulose-based desserts.

Dieckol is a brown algal phlorotannin with potent bioactivities such as hepatoprotective effects. This study aimed to produce dieckol-rich extract from Eisenia bicyclis and apply them as a functional ingredient for a novel bacterial cellulose (BC)-based dessert. The modeling and optimization of the ultrasound-assisted extraction (UAE) process were performed. The developed model showed that at least one of the linear or quadratic terms of all variables (amplitude, solvent concentration, temperature, and time) affected dieckol recovery (p<0.05). Following numerical optimization, dieckol recovery achieved 17.3mg/g biomass under optimal conditions (62% amplitude, 35% solvent concentration, 70°C, and 6min), similar to the model-predicted value (18.3mg/g). Then, BC-dieckol (BC-D) pellicles were fabricated by ex situ loading 25, 50, 75, and 100% of the liquid extract with 9.954mg dieckol (recommended intake level). All BC-D pellicles showed high dieckol loading (98.16-100%), and BC-D100 meets the acceptable level. The FTIR and SEM analyses confirmed the impregnation of the extract, especially the signature peaks of dieckol, related to aromatic ether stretch and aromatic ring structure of phlorotannin by the FTIR spectra. The crystallinity index and water absorption capacity of BC-D100 were determined to be about 83.9% and 415.6%, respectively, which were slightly decreased compared to BC-control, but not significantly. In addition, BC-D100 has shown antioxidant activity making it valuable as a food material. This study provides valuable information for the food industry, paving the way for developing novel BC-based foods using brown algal extracts.

Supercritical carbon dioxide extraction of Polygonum cuspidatum powders: Experiments and modelling

AbstractIn recent years, the natural anticancer components resveratrol and emodin have attracted significant attention. This study employs supercritical carbon dioxide (ScCO2) extraction, using ethanol as a cosolvent, to extract resveratrol and emodin from Polygonum cuspidatum powders. Experiments were conducted at temperatures ranging from 308 to 328 K, pressures from 15 to 30 MPa, ethanol contents of 60 to 160 mL/L, and extraction times between 1800 and 7200 s. The maximum extraction yields of resveratrol and emodin were 2.516 and 2.765 mg/g, respectively, under optimal conditions (temperature: 323 K, pressure: 25 MPa, ethanol content: 100 mL/L, extraction time: 3600 s), determined through one‐way experiments. Additionally, a mathematical model of the ScCO2 extraction process was developed. The mass transfer coefficient (Kf) was used as a fitting parameter and the kinetic model, based on mass conservation, was validated with experimental data. The model demonstrated good accuracy, with a low average absolute relative deviation (AARD) of 4.05%. This model provides theoretical support for industrial scaling and process optimization, achieving maximum extraction efficiency while minimizing CO₂ and ethanol consumption, thereby reducing costs and enhancing environmental benefits. Its establishment framework and methodology offer valuable references for optimizing similar ScCO2 extraction processes.

Modeling of polyphenols extraction process from green tea and the effect of temperature on the process

Modeling of polyphenols extraction process from green tea and the effect of temperature on the process

Modelling of Humic Acids Extraction Process Assisted by Low-Intensity Ultrasound with the Use of Three-Level Fractional Factorial Design

Modelling of Humic Acids Extraction Process Assisted by Low-Intensity Ultrasound with the Use of Three-Level Fractional Factorial Design

Simulation and Experimental Study of Solid-Liquid Extraction of Coal Tar Residue Based on Different Extractants.

Coal tar residue (CTR) is recognized as a hazardous industrial waste with a high carbon content and coal tar consisting mainly of toxic polycyclic aromatic hydrocarbons (PAHs). The coal tar in CTR can be deeply processed into high-value-added fuels and chemicals. Effective separation of coal tar and residue in CTR is a high-value-added utilization method for it. In this paper, ethyl acetate, ethanol, and n-hexane were chosen as extractants to study the extraction process of coal tar from CTR, considering the mass transfer in the liquid phase outside the CTR particles and the diffusion inside the CTR particles, and a mathematical model of the solid-liquid extraction process of CTR was established based on Fick's second law. First, the mass-transfer coefficients (kf) and effective diffusion coefficients (De) of ethyl acetate, ethanol, and n-hexane in solid-liquid extraction at 35 °C were determined to be 1.54 × 10-5 and 4.99 × 10-10 m2·s-1, 1.14 × 10-5 and 3.57 × 10-10 m2·s-1, and 1.01 × 10-5 and 3.48 × 10-10 m2·s-1, respectively. Furthermore, the simulated values obtained by the model also maintained a high degree of agreement with the experimental results, which indicates the high accuracy prediction of the model. Finally, the model was used to investigate the effects of the solvent-solid ratio, temperature, and stirring speed on the extraction rates with the three extractants. According to the analysis with gas chromatography-mass spectrometry (GC-MS), among the three solvents, n-hexane extracted the highest content of aliphatic hydrocarbons (ALHs), ethyl acetate extracted the highest content of oxygenated compounds (OCs), and ethanol extracted the highest content of aromatic hydrocarbons (ARHs). The model and experimental data can be used to provide accurate predictions for industrial utilization of CTR.

Pattern Based Sequence Classification

The text highlights the challenges of analyzing massive amounts of data, particularly in the context of business processes using process mining. Outliers or irregular behavior in the data can negatively impact processing and clutter process models, leading to less useful paths. The objective is to automatically extract process models from the data, and an automated method for removing irregular behavior from event logs is introduced. This method significantly improves the quality of identified process models and scales well to large datasets. Since the effectiveness of filtering strategies depends on the event log, users can interactively filter activities and directly view the filtered process model from the event log using a slider-based approach. Ultimately, the choice of included activities is left to the user. The method is tested using actual occurrence log collections from enterprise process oversight and hospital environments. The results demonstrate that the newly proposed activity filtering approaches yield process models that are more behaviorally specific compared to conventional frequency-based filtering methods

Study of heat and mass transfer processes during extraction of plant raw materials under the influence of ultrasound

Theoretical analysis of the process of biologically active substances (BAS) extraction from plant raw materials in conditions of ultrasonic action and without it to describe the kinetics of the process has been conducted. A mathematical model of the process of BAS extraction from plant raw materials to establish the dependence of changes in the concentration of BAS in the volume of cells in the intercellular space and in the main volume of the extractant has been developed. On the basis of the solution of the mathematical model the duration of the model of BAS extraction process from plant raw materials has been established, results show that the duration of the process of extraction of oil from plant raw materials in an acoustic extractor decreases by 1.5 times ultrasonic extraction can be used for the extraction of biologically active substances, such as essential oils, lipids and dietary supplements from plants.

Deep learning assisted physics-based modeling of aluminum extraction process

Modeling complex physical processes such as the extraction of aluminum is mainly done using pure physics-based models derived from first principles. However, the accuracy of these models can often suffer due to a partial understanding of the process, uncertainty in the input parameters, and numerous modeling assumptions. More recently, with the ever-increasing availability of data, there has been an explosion of interest in applying modern machine learning methods because of their ability to learn complex mappings directly from data. Unfortunately, these models tend to be black boxes, require an enormous amount of data, and do not utilize existing domain knowledge. In this work, we develop a novel approach combining physics-based and data-driven modeling approaches while eliminating some weaknesses. We use a data-driven model to correct a misspecified physics-based model of the Hall–Héroult process in an aluminum electrolysis cell using a corrective source term added to the set of governing ordinary differential equations. Our approach ensures that the existing knowledge is utilized to the maximum extent possible while relying on the data-driven models only to model those aspects which the physics-based model does not represent well. We compare this approach with an end-to-end learning approach and an ablated physics-based model, showing that the proposed hybrid method is more accurate, consistent, and stable for long-term predictions.

Comparison of Single Control Loop Performance Monitoring Methods

Well-performing control loops have an integral role in efficient and sustainable industrial production. Control performance monitoring (CPM) tools are necessary to establish further process optimization and preventive maintenance. Data-driven, model-free control performance monitoring approaches are studied in this research by comparing the performance of nine CPM methods in an industrially relevant process simulation. The robustness of some of the methods is considered with varying fault intensities. The methods are demonstrated on a simulator which represents a validated state-space model of a supercritical carbon dioxide fluid extraction process. The simulator is constructed with a single-input single-output unit controller for part of the process and a combination of relevant faults in the industry are introduced into the simulation. Of the demonstrated methods, Kullback–Leibler divergence, Euclidean distance, histogram intersection, and Overall Controller Efficiency performed the best in the first simulation case and could identify all the simulated fault scenarios. In the second case, integral-based methods Integral Squared Error and Integral of Time-weighted Absolute Error had the most robust performance with different fault intensities. The results highlight the applicability and robustness of some model-free methods and construct a solid foundation in the application of CPM in industrial processes.

Video Process Mining and Model Matching for Intelligent Development: Conformance Checking.

Traditional business process-extraction models mainly rely on structured data such as logs, which are difficult to apply to unstructured data such as images and videos, making it impossible to perform process extractions in many data scenarios. Moreover, the generated process model lacks analysis consistency of the process model, resulting in a single understanding of the process model. To solve these two problems, a method of extracting process models from videos and analyzing the consistency of process models is proposed. Video data are widely used to capture the actual performance of business operations and are key sources of business data. Video data preprocessing, action placement and recognition, predetermined models, and conformance verification are all included in a method for extracting a process model from videos and analyzing the consistency between the process model and the predefined model. Finally, the similarity was calculated using graph edit distances and adjacency relationships (GED_NAR). The experimental results showed that the process model mined from the video was better in line with how the business was actually carried out than the process model derived from the noisy process logs.

Investigation of reactive extraction of monocarboxylic acids with menthol-based hydrophobic deep eutectic solvent by response surface methodology

ABSTRACT The growing demand for producing organic acids by fermentative techniques has increased the significance of separating carboxylic acids from their fermentation broth with the reactive extraction process. Considering the environmental impacts, deep eutectic solvents can be considered as a potential green alternative for the replacement of volatile organic solvents commonly used in the extraction process. In this study, a new type of green solvent named hydrophobic deep eutectic solvent (HDES) based on decanoic acid as a hydrogen bond acceptor and menthol as a hydrogen bond donor was utilized for the reactive extraction of formic, acetic, and propionic acids from their aqueous solutions. The effect of initial acid concentration, HDES molar ratio, and tri-n-octyl amine (TOA) concentration on extraction efficiency was investigated. Modeling of the reactive extraction process was performed via a response surface methodology with a central composite design. Herein, the effect of the parameters of TOA concentration, HDES molar ratio, and initial acid concentration on the distribution coefficient was investigated. According to the results, it was reported that the most effective parameter on the extraction efficiency (%E) was the amount of extractant. The results of the experimental studies showed that the highest separation efficiency was obtained for 5% initial concentrations of formic, acetic, and propionic acids by using a mixture of 0.5 HDES molar ratio solvent and 1.9 mol/L TOA. The extraction efficiencies of these acids were found to be 88.71, 92.52, and 95.90 with ±0.1 standard deviation, respectively.

Optimization, Kinetic and Phenomenological Modeling of Ultrasound-Assisted Extraction Process of Bioactive Compounds from Raspberries (Rubus idaeus L.)

Bioactive compounds are of considerable interest due to their antioxidant properties and potential beneficial health effects. Thus, this study aimed to optimize and model the ultrasound-assisted extraction process (UAE) of response variables total anthocyanins (TA) and total phenolic content (TPC) from raspberries, and to compare the optimized extraction process with the conventional one. The variables used were time (5 to 75 min), temperature (30 to 70 °C), and solid: liquid ratio (1:5 to 1:15 m/v), applied to the Box-Behnken Design. The optimal condition of the UAE process occurs at a temperature of 70 °C and a solid: liquid ratio of 1:12.5 m/v, for both response variables. The optimal time for extraction of TA occurs in 22.5 min predicting the content of 23.181 mg/100 g and for TPC occurs in 57.5 min predicting the content of 156.30 mg GAE/100 g. On validation of the optimized conditions, less than a 5% difference was found between the predicted and experimental values (24.131 mg/100 g for TA, and 149.226 mg GAE/100 g for TPC). When comparing the optimized UAE with the conventional extraction, it wares observed that UAE increased (p < 0.05) the extraction of TA content by 18.28% and TPC by 28.88%. The process time reduction from 24 h in conventional extraction to less than 1 h in optimized UAE stands out. Furthermore, the Film Theory model tested fitted well to the extraction process under study. Thus, the study indicates that the UAE process is an efficient green methodology for recovering bioactive compounds from raspberries.

Cellulose from the banana stem: optimization of extraction by response surface methodology (RSM) and charaterization

Cellulose was extracted from the banana stem by chemical method and the factors affecting the extraction process such as concentration of NaOH and H2O2, as well as the assisted microwave time were investigated. Design-Expert software with Response Surface Methodology was used in the modeling and optimization of the cellulose extraction process. The results of XRD, FT-IR, SEM were also used to determine the physicochemical properties of cellulose obtained from the banana stem. The results of the modeling and optimization process of cellulose extraction showed the efficiency of the process and the high applicability of cellulose from the banana stem to create valuable industrial products.

Kinetic study in the extraction of xanthones from Securidaca longepedunculata roots by microwave-assisted-extraction

BackgroundExtraction of active compounds from plants using microwave can be utilized as an alternative solution for conventional extraction. To well understand this technology, the study of the modelization and kinetics mechanism of the extraction method is necessary. This study aimed to evaluate the suitable kinetics models for MAE of the Xanthones from Securidaca longepedunculata Fresen roots.ResultsThe experimental data (xanthones versus time, power and ratio, respectively) were fitted to three-parameter empirical kinetics models. The second-order models appear to be the best fit to explain kinetics study of MAE than the first model. The second-order model was acceptable, with elevated value of the correlation coefficient (R2 = 0.9861), showing that it perfectly relates the process.ConclusionBased on the results obtained, the extraction of xanthones from roots of S. longepedunculata is done successively in two steps, washing and diffusion of compounds from matrices as described by second-order kinetics model. The latter could report the kinetics model of extraction process from S. longepedunculata Fresen using microwave technology.

Stable heuristic miner: Applying statistical stability to discover the common patient pathways from location event logs

• A novel algorithm for discovering the descriptive reference process model. • Application of the statistical stability phenomenon to extract process models. • Removing the arbitrary selection of the thresholds’ values in the heuristic miner. • Automatically extraction of deviating behaviors from location event logs. Purpose: The classic heuristic miner algorithm has received lots of attention in the healthcare sector for discovering patient pathways. The extraction of these pathways provides more transparency about patient activities. The previous versions of this algorithm receive an event log and discover several process models by using manually adjustable thresholds. Then, the expert is left with the difficult task of deciding which discovered model can serve as the descriptive reference process model. Such a decision is completely arbitrary and it has been seen as a major structural issue in the literature of process mining. This paper tackles this problem by proposing a new process discovery algorithm to facilitate patient pathways diagnosis. Approach: To address this scientific challenge, this paper proposes to consider the statistical stability phenomenon in an event log, and it introduces the stable heuristic miner algorithm as its contribution. To evaluate the applicability of the proposed algorithm, a case study has been presented to monitor patient pathways in a medical consultation platform. Originality: Thanks to this algorithm, the value of thresholds will be automatically calculated at the statistically stable limits . Hence, instead of several models, only one process model will be discovered. To the best of our knowledge, applying the statistical stability phenomenon in the context of process mining to discover a reference process model from location event logs has not been addressed before. Findings/Practical implications: The results enabled to remove the uncertainty to determine the threshold that represents the common patient pathways and consequently, leaving some room for potential diagnosis of the pathways.

An eco-friendly and sustainable system for monitoring the oleuropein-rich extract from olive tree (Olea europaea) leaves

In this study, different combinations (1/1, 1/2, and 2/1 of molar ratio) of deep eutectic solvents were designed by combining a hydrogen bond acceptor (carboxylic acids such as citric and lactic acids) and a hydrogen bond donor (glycerol, ethylene glycol, ammonium and sodium acetates). Their performance was measured with respect to their success in extracting oleuropein-rich extract from olive leaves via homogenizer-assisted extraction. Lactic acid/glycerol (1/1) has been observed to be the most successful deep eutectic solvent. A split-plot central composite design was employed to observe the effects of the process parameters (water addition, speed and extraction time) on the total phenolic and oleuropein levels in the extract as well as modelling and optimization of the homogenizer-assisted extraction process. The most significant process parameter was found to be water addition (P < 0.0001). The optimum conditions were calculated as 90 s of extraction time under 14000 rpm with 50% water in the selected solvent system to achieve the highest total phenolic (39.41 mg-GAE/g-DL) and oleuropein (14, 06 mg/g-DL) yields. The proposed green solvent has been also monitored to be a sustainable extraction media depending on the stability test outcome, where the samples were kept stable for almost 2 months at 25°C, while 6 months were observed to be the storage time without degradation in colder environments.

GPROFIT: A Tool to Assist the Automatic Extraction of Business Knowledge From Legacy Information Systems

Business digitization is a crucial strategy for business growth in the 21st century. Its benefits include improving business process automation, customer satisfaction, productivity, decision-making, turnover, and adaptation to market changes. However, digitization is not a trivial task. As a major paradigm and mindset shift, it involves a lot of effort within an organization and therefore requires commitment from employees and managers. This is especially critical in companies whose business processes are mostly reliant on legacy information systems (LIS), which are usually specialized and based on technological architectures that could be considered obsolete. The replacement of these systems by more recent, process-oriented technologies, the building up of employees' know-how and the continued use of outdated documentation are difficult, expensive tasks that hinder the initiation of continuous improvement processes in companies. This paper proposes techniques for finding and extracting process models from legacy databases. Specifically, it ( i) lays the theoretical foundations of a model-driven framework for systematically extracting business process models (conform to standard BPMN notation) from LIS considering process time perspective, and (ii) proposes a technological tool called gPROFIT, which uses machine learning techniques to support that theoretical framework, facilitate its use in real environments and extract the business knowledge embedded in such legacy systems. The paper also presents proofs-of-concept showing how our proposal has been validated in several legacy systems.

MODELING OF EXTRACTION PROCESS IN THE SYSTEM SOLID BODY–LIQUID

The paper reports the basic principles of modeling of extraction process through the cell walls. A solid body is assumed to be an aggregate of deformable particles, inside of which there is an extractable substance. Two stages of the process are considered: diffusion and hydrodynamic ones. The mass exchange stage inside the solid is diffusive because it has not fluidity. As far as the diffusion stage is concerned, we propose to consider the process of internal transfer as a set of pseudocatalytic reactions between the solvent molecules and the extractable substance. Based on this approach, the relationship between the diffusion coefficient and the particles structure has been derived under the condition of a cellular structure of the particles. The equations for describing the time change of the mean concentrations of a substance are given, taking into account the effect of the external mass discharge condition at interphase boundaries. It has been proved that the applied approach makes it possible to find a sufficient number of parameters for describing the different effects of extraction and gives physical meaning to interpretations of kinetic curves of extraction. As regards the hydrodynamic stage, a proper frame model of the pore space was formulated, which is deterministic and two-scale. It takes into account possible shear and volume deformations of the particles. Such two-phase medium was considered as a collection of variable intensity sources. The equations for calculating the mass transfer coefficient for the system are solid particles – liquid, taking into account the rheology of the liquid phase as a mixture of extractant and extractable substance. It has been proved that the proposed models have sufficient universality to withstand adaptation to different specific situations.

Process modeling of solvent extraction of oil from Hura crepitans seeds: adaptive neuro-fuzzy inference system versus response surface methodology

Vegetable oils are a very important feedstock for many industries such as biofuels. There is the need to source for novel and underexploited plant oilseeds to meet the world demand for oils. Thus, the extraction of oil from Hura crepitans (sandbox) seeds was conducted using the solvent extraction method. Modeling of the extraction process was carried out using response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). The effects of the nature of the solvent (non-polar (n-hexane) and polar (acetone and ethyl acetate)), solid-solvent ratio (0.1–0.3 g/mL), extraction time (2–6 h), and their interactions on the oil yield were investigated using the D-optimal design technique. Performance assessment of the developed models was carried out to check their effectiveness in predicting the H. crepitans seed oil (HCSO) yield using various fit statistics. The coefficient of determination (R2) observed for the RSM and ANFIS models was 0.9720 and 0.9988, respectively, with corresponding mean relative percent deviation (MRPD) of 2.50 and 0.37%. Maximum HCSO yield of 62.95 wt% was achieved by ANFIS coupled with genetic algorithm (GA) using 0.1 g/mL solid-solvent ratio, extraction time of 4.19 h, and acetone, while maximum HCSO yield of 62.50 wt% was observed by RSM with a solid-solvent ratio of 0.1 g/mL, extraction time of 4.04 h, and acetone. Characteristics of the HCSO indicated that it could serve as a good feedstock for the production of oleochemicals such as biodiesel. The results obtained in this study demonstrated that ANFIS is marginally superior to RSM in the modeling of the HCSO extraction process, while GA was slightly better than the numerical tool of RSM in the optimization of the process.

Ionic liquid-based ultrasonic-assistedextraction model for solvent design

Ionic liquid is a favourable solvent in separation process due to the vast possibilities of ionic liquid structures. Many researches showed that ionic liquid extract higheramount of phytochemicals compared to conventional solvents. Even better, ionic liquid extraction coupled with ultrasonic-assisted extraction produced higher yield and less hassle. Therefore, ionic liquid can be considered as a suitable solvent for herbal extraction. This study is a part of a solvent design framework to obtain an extraction process model. It focuses on the process of ionic liquid-based ultrasonic-assisted extraction of flavonoid and phenolic acid from Labisiapumila. A two level, four factor of central composite design (CCD) was employed to determine the effect of the process factors towards yield of flavonoid and phenolic acid extracted from the herb. The process factors are the temperature (Celsius), extraction time (min), power (W) and type of ionic liquid (based on their dielectric point). The extracted samples were analysed to determine the yield of flavonoid and phenolic acid. Two models were developed for flavonoid and phenolic acid extraction based on the optimization results. The models are important as yield prediction in solvent process performance stage of the development of ionic liquid design framework.