37 research outputs found
Development and Analysis of Group Contribution Plus Models for Property Prediction of Organic Chemical Systems
No full textPrediction of properties is important in chemical process-product design. Reliable property models are needed for increasingly complex and wider range of chemicals. Group-contribution methods provide useful tool but there is a need to validate them and improve their accuracy when complex chemicals are present in the mixtures. In accordance with that, a combined group-contribution and atom connectivity approach that is able to extend the application range of property models has been developed for mixture properties. This so-called Group-ContributionPlus (GCPlus) approach is a hybrid model which combines group contribution and molecular descriptor theories (such as connectivity indices – CI). Connectivity indices are formalisms defined via graph theoretical concepts intended to describe the topological characteristics of molecular structures. The main idea is the use of connectivity indices to describe the molecular fragmentation that relates properties which is the molecular interactions with the molecular structures. One well known and established group-contribution method is the UNIFAC model, used to predict liquid phase activity coefficients for mixtures. The needed values of the group interaction parameters (GIPs) are obtained by fitting phase equilibrium data. There are, however many gaps in the UNIFAC parameter table due to lack of data. Alternative to performing measurements, which may not be feasible, values of the missing GIPs, can be predicted through the GCPlus approach. The predicted values for the GIPs are then used in the UNIFAC model to calculate activity coefficients. This approach can increase the application range of any “host” UNIFAC model by providing a reliable predictive model towards fast and efficient product development. This PhD project is focused on the analysis and further development of the GCPlus approach for predicting mixture properties to be called the UNIFAC-CI model. The contributions of this work include an analysis of the developed Original UNIFAC-CI model in order to investigate why the model does not perform as well as the reference UNIFAC model for some systems while performing surprisingly better than the reference model for other systems. In this analysis, it is found that by introducing more structural information to the CHO group through the valence connectivity index (CI), the correlation error involving alkanes-aldehydes system can be reduced. This work is presented in Chapter 3. Furthermore in Chapter 4, as a continuation of the analysis done for systems involving C, H and O atoms, the Original UNIFAC-CI (VLE) model has been further reused and significantly expanded by including nitrogenated, chlorinated and sulfurated systems and the involved atom interaction parameters (AIPs) have been regressed. In addition to that, another set of parameters have been generated for the Original UNIFAC-CI (VLE) model using a quality assessment algorithm, QVLE (combination of 4 VLE consistency tests) as a weighting factor for each VLE dataset in the objective function for regression of AIPs. The quality factors are useful in identifying anomalous systems which can be problematic in the parameter estimation and can produce parameters which are not accurately representing the systems used for the regression. Moreover, in Chapter 5 the Original UNIFAC-CI (VLE/SLE) model have been developed where the atom interaction parameters (AIPs) are obtained through regression against both VLE and SLE experimental data. The prediction accuracy of SLE systems using the regressed parameters has been slightly increased. Besides that, in Chapter 6, Modified (Dortmund) UNIFAC-CI has been further developed by including chlorinated and sulfurated VLE systems. Finally, in Chapter 7, the developed Original UNIFAC-CI (VLE/SLE) model has been highlighted in selected case studies involving the design of a working solution for hydrogen peroxide production and solubility investigation of pharmaceutical systems where new group have been created and their interaction parameters are predicted/fine tuned generating a master parameter table specifically for those case studies. Also, the applicability of the Original UNIFACCI model is shown for predicting phase equilibria of lipid systems, filling missing GIPs and improving prediction of azeotropic mixture. In Chapter 8, a discussion with concluding remarks and recommendation for future work are presented
Potential of bio-products adsorption by immobilized metal ion affinity mesoporous adsorbents
No full textA separation process which involves the extraction of bio-products such as protein and antibiotics are called bio-separation processes. Bio-products are chemical substances or a combination of chemical substances that are made by living things and nature. They can be derived or extracted from whole plants and animal or by synthesis in bioreactors containing cells and enzymes. These bio-products are valuable in terms of their chemical activity such as methanol for its solvent activity, penicillin for its antibacterial activity, taxol for its anti-cancer activity and many more to note. This wide variety of bioproducts with different nature produces a wide range of bio-separation technologies and thus factors such as the nature of the products, purity, yield and activity will determine which bio-separations technology are the most suitable to apply. The biotechnology industry which started in the late 1970s increases the importance of bio-products as many products of biotechnology are proteins and antibiotics that are usually hard to purify leading to high cost (Harrison et al., 2003). In addition, of all the bio-separation technologies available today, some have their limitations including poor product yield, inadequate separation selectivity, high cost and many more. Therefore efforts to overcome these limitations must be made by attempting to modify and improve the existing bio-separation processes. Throughout this topic, the potential of adsorption based bio-separation technology will be discussed to extract protein and antibiotic by using mesoporous adsorbents, MCM-41 and SBA-15 incorporated with intermediate metal ions
Analysis and Application of GC Plus Models for Property Prediction of Organic Chemical Systems
No full textIn this paper, a detailed analysis of the performance and trends of predictions of vapour–liquid phase equilibrium with the UNIFAC-CI model, employing a method to predict missing group interaction parameters (GIPs) through the use of connectivity indices, are presented. The cases where the model using the predicted GIPs perform well and cases where the performance is unreliable are investigated. The causes for the unreliable performance of the UNIFAC-CI model are explained and results from one of the remedies that gave very good results are presented. The extrapolation features of the UNIFAC-CI model with the predicted GIPs in solid–liquid phase equilibria calculations involving precipitation of organic chemicals are also presented. Finally, the application of the GCPlus approach to reference modified UNIFAC (Dortmund) model is presented in terms of new and extended parameter tables
Development and analysis of the Original UNIFAC-CI model for prediction of vapor–liquid and solid–liquid equilibria
No full textIn this work, we present a further development and analysis of the Original UNIFAC-CI models for the prediction of vapor–liquid equilibrium (VLE) and solid–liquid equilibrium (SLE) for a wide range of mixtures. Three sets of atom interaction parameters (AIPs) have been regressed. For the first two sets, only VLE experimental data were used in parameter estimation. In the first set, no weighting factors were used for each of the VLE data in the objective function when regressing the AIPs. However, for the second set, the AIPs have been regressed using the so-called QVLE quality factors obtained for each of the VLE data from a quality assessment algorithm (consistency tests) as weighting factors in the objective functions. For the third set of parameters, SLE and VLE data were used in the regression of AIPs. The result of the correlations in terms of deviations errors and predictions using these three sets of regressed parameters are presented, compared and discussed. The significance of adding the QVLE values and SLE systems in the regression of the AIPs are also highlighted. UNIFAC is a model that can be in principle used for both VLE and SLE (as well as other types of phase behavior) calculations. The range of applicability of the predictive UNIFAC-CI is investigated and it is shown to what extent the Original UNIFAC-CI model can successfully predict SLE especially when the needed parameters are missing
Application of the UNIFAC-CI Model for Phase Equilibria Predictions of Organic Chemical System
No full textNatural deep eutectic solvent (NADES) design framework for extraction of polyphenols from jackfruit
No full textJackfruit considered to be an underutilized fruit where around 60 % of it was dumped and this creates a critical waste disposal which highly affects the environment. NADES have been widely used in many applications and it has emerged as a new sort of green solvents used to replace the organic solvents with numerous advantages like simple preparation, sustainability and environmentally friendly. The main aim of this research is to develop a framework of NADES design for extraction of polyphenols from jackfruit by using computational approach combined with mathematical models. The research methodology is divided into 3 stages. Stage 1 is mainly about problem definition which required determining the needs for NADES and setting its target property values. Stage 2 is mainly focus on the development of databases which involving the prediction of target property values for both the NADES and polyphenols by either calculating using suitable property model which corresponding to the target properties or using computational approach such as COSMOtherm and TmoleX to obtain the missing information. In Stage 3, first screening process is based on the target properties of NADES and followed by second screening process which is based on their solvation performance in extraction of polyphenols and top 4 NADES were selected. This proposed method has been applied in a case study to select the top 4 NADES for the extraction of quercetin, artocarpin and gallic acid from jackfruit. From the case study, the relative solubility value of NADES 33 in quercetin, artocarpin and gallic acid from jackfruit is the highest which are 0.28547, 78.3101 and 0.0095 g/g. It showed that NADES 33 was the best extraction solvent for the polyphenols extraction in current database out from 51 NADES candidates
Tailor-made green diesel blends design using a decomposition-based computer-aided approach
No full textIn this study, the tailor-made green diesel blend design problem is mathematically formulated and solved by a decomposition-based computer-aided approach. The green diesel design problem is solved in three main stages to identify the feasible green diesel blend candidates that meet the product property constraints (density, kinematic viscosity, cetane number, higher heating value and flash point) with the desired performance criteria. An optional additives identification step is introduced to enhance the blends. The shortlisted green diesel blends are evaluated on the basis of cost, cetane number and higher heating value. To ensure that the shortlisted candidates have acceptable functional reliability, their compatibility with the engine compartment, engine performance, and emission requirements should be addressed in future works
Production of Dialkly Carbonates Via Reactive-Extractive and Pressure-Swing Distillations Using Unifac-CI VLE Model Predictions
No full textDevelopment and Analysis of Original UNIFAC-CI and Modified UNIFAC-CI Models for Prediction of VLE and SLE Systems
No full textPrediction of properties is important in chemical process-product design. Group-contribution (GC) methods provide useful tool but there is a need to validate and improve their accuracy when complex chemicals are present in the mixtures. In accordance with that, a combined GC and atom connectivity approach that is able to extend the application range of property models has been developed for mixture properties. This so-called GCPlus approach is a hybrid model which combines GC and valence connectivity indices (CI). The main idea is the use of CI to describe the molecular fragmentation that relates properties, the molecular interactions with the molecular structures. One established GC method is the UNIFAC model to predict liquid phase activity coefficients. The needed values of the group interaction parameters (GIPs) are obtained by fitting phase equilibrium data. There are many gaps in the UNIFAC parameter table due to lack of data. Alternative to performing measurements, values of the missing GIPs, can be predicted through the GCPlus approach. The predicted values for the GIPs are then used in the UNIFAC model to calculate activity coefficients. In this work, the model parametersfor using the GCPlus approach to the original UNIFAC and Modified (Dortmund) UNIFAC have been regressed against vapor-liquid equilibrium (VLE) data and simultaneously against VLE and solid-liquid equilibrium (SLE) data for groups formed by C, H, O, N, Cl and S atoms. Initially the VLE data used to regress those parameters are checked using a quality assessment algorithm which combines four widely used consistency tests (Herington, Van Ness, Point/Differential and Infinite Dilution tests) and also a check on the consistencies of the data with the pure component vapor pressures. The overall quality factors, QVLE obtained for each dataset indicate the quality of each datasets and can then be used as weighting factors, in the objective function for the parameter regression with VLE data (and with SLE data). The performance of the CI-models using parameters regressed against VLE data and simultaneously against VLE and SLE data are compared in terms of the uncertainties of the parameters regressed against the predicted properties and the accuracy of the predictions. In addition, the model performances are compared with their reference UNIFAC models
Ionic liquid solvent design framework for extraction of phytochemicals using microwave-assisted method
No full textComputer aided molecular design (CAMD) has been used widely for solvent design. It is a reverse approach in the selection of solvents for real application. CAMD is suitable for ionic liquid solvent design due the vast possibilities of ionic liquid molecular structures to be identified. Ionic liquid has broad ranges of applications especially in separation especially in carbon capture and azeotropic separation. This is due to the unique structures of ionic liquid that can be tailored for specific product separation. This study focuses on ionic liquid design framework for phytochemical extraction, incorporating the prediction of the extraction yield using the microwave-assisted method. The framework was developed in several stages. Stage 1 identifies the user needs, problems and constraints as well as target properties. Stage 2 comprise of comprehensive database development for ionic liquid and phytochemical properties; while property models' library was developed in Stage 3. Stage 4 involved the development of solvent design algorithm for ionic liquid selection for the targeted process. In Stage 5, depending on the type of extraction method considered, either using normal Soxhlet or a microwave-assisted extraction, the extraction yield can be predicted using the process performance model. The performance model for the liquid extraction is the thermodynamic solid-liquid equilibria model while for the advanced extraction method, the model was obtained through optimization of the experimental extraction process. This systematic framework is illustrated through a case study involving flavonoid and phenolic acid extraction from Ortosiphon aureus. Based on the yield prediction, 1-ethyl-3- propylimidazoilum bromide can extract 29.92 mg/g and was selected as a solvent for Flavonoid extraction using Microwave-assisted extraction. The design framework is able to find the optimal ionic liquid candidate for the extraction process
