Computer-Aided Ionic Liquid Design and Experimental Validation for Benzene-Cyclohexane Separation

For the purpose of designing ionic liquid (IL) solvents for the extractive separation of benzene/cyclohexane at 298.15 K, a computer-aided ionic liquid design (CAILD) method is developed. The UNIFAC-IL model is used to calculate the thermodynamic properties, while the group contribution (GC)-based methods are employed to estimate the physical properties and toxicity. A mixed-integer nonlinear programming (MINLP) problem is formulated, and the top five IL solvents are obtained by the BONMIN algorithm. One of the designed ILs [COC2MIM][Tf2N] (1-(2-methoxyethyl)-3-methylimidazolium bis(trifluoromethylsulfonyl)imide) is selected to perform the liquid-liquid extraction (LLE) experiment, and the distribution ratio and selectivity at 0.1 molar concentration of benzene in the raffinate phase are 1.20 and 17.10, respectively. The comparison with other solvents shows that the designed IL not only has an excellent separation performance but also favorable physical and environmental properties. After regressing the parameters for the NRTL model, the process simulation using the designed IL is developed by Aspen Plus, and the results are compared with that of the benchmark organic solvent sulfolane. The IL-based process needs 32475.1 kg/h of solvent and 1283 kW of heat duty, while the sulfolane-based process uses 43998.5 kg/h of solvent and 6296 kW of heat duty. These results demonstrating [COC2MIM][Tf2N] is a promising alternative to conventional solvents for the extractive separation of benzene/cyclohexane

Computer-aided design of optimal environmentally benign solvent-based adhesive products

The manufacture of improved adhesive products that meet specified target properties has attracted increasing interest over the last decades. In this work, a general systematic methodology for the design of optimal adhesive products with low environmental impact is presented. The proposed approach integrates computer-aided design tools and Generalised Disjunctive Programming (GDP), a logic-based framework, to formulate and solve the product design problem. Key design decisions in product design (i.e., how many components should be included in the final product, which active ingredients and solvent compounds should be used and in what proportions) are optimised simultaneously. This methodology is applied to the design of solvent-based acrylic adhesives, which are commonly used in construction. First, optimal product formulations are determined with the aim to minimize toxicity. This reveals that number of components in the product formulation does not correlate with performance and that high performance can be achieved by investigating different number of components as well as by optimising all ingredients simultaneously rather than sequentially. The relation between two competing objectives (product toxicity and concentration of the active ingredient) is then explored by obtaining a set of Pareto optimal solutions. This leads to significant trade-offs and large areas of discontinuity driven by discrete changes in the list of optimal ingredients in the product

Perspectives on Resource Recovery from Bio-Based Production Processes: From Concept to Implementation

Recovering valuable compounds from waste streams of bio-based production processes is in line with the circular economy paradigm, and is achievable by implementing “simple-to-use” and well-established process separation technologies. Such solutions are acceptable from industrial, economic and environmental points of view, implying relatively easy future implementation on pilot- and full-scale levels in the bio-based industry. Reviewing such technologies is therefore the focus here. Considerations about technology readiness level (TRL) and Net Present Value (NPV) are included in the review, since TRL and NPV contribute significantly to the techno-economic evaluation of future and promising process solutions. Based on the present review, a qualitative guideline for resource recovery from bio-based production processes is proposed. Finally, future approaches and perspectives toward identification and implementation of suitable resource recovery units for bio-based production processes are discussed

Combining artificial intelligence and robotic system in chemical product/process design

Product design for formulations is an active and challenging area of research. The new challenges of a fast-paced market, products of increasing complexity, and practical translation of sustainability paradigms require re-examination the existing theoretical frameworks to include the advantages from business and research digitalization. This thesis is based on the hypotheses that (i) new products with desired properties can be discovered by using a robotic platform combined with an intelligent optimization algorithm, and (ii) we can the connect data-driven optimisation with physico-chemical knowledge generation, which will result in a suitable model for translation of product discovery to production, thus impacting on the process development steps towards industrial applications. This thesis focuses on two complex physicochemical systems as case studies, namely the oil-in-water shampoo system and sunscreen products. Firstly, I report the coupling of a machine-learning classification algorithm with the Thompson-Sampling Efficient Multi-Optimization (TSEMO) for the simultaneous optimization of continuous and discrete outputs. The methodology was successfully applied to the design of a formulated liquid product of commercial interest for which no physical models are available. Experiments were carried out in a semi-automated fashion using robotic platforms triggered by the machine-learning algorithms. The proposed closed-loop optimization framework allowed to find suitable recipes meeting the customer-defined criteria within 15 working days, outperforming human intuition in the target performance of the formulations. The framework was then extended to co-optimization of both formulation and process conditions and ingredients selection. Secondly, I report the methods for the identification of new physical knowledge in a complex system where a prior knowledge is insufficient. The application of feature engineering methods in sun cream protection prediction was discussed. It was found that the concentration of UVA and UVB filters are key features, together with product viscosity, which match with the experts’ domain knowledge in sun cream product design. It was also found that through the combination of feature engineering and machine learning, high-fidelity model could be constructed. Furthermore, a modified mixed-integer nonlinear programming (MINLP) formulation for symbolic regression method was proposed for identification of physical models from noisy experimental data. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variables.The methodology was proven to be successful in identifying the correct physical models describing the relationship between shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of chemical reactions. The work of this thesis shows that machine learning methods, together with automated experimental system, can speed-up the R&D process of formulated product design as well as gain new physical knowledge of the complex systems

Computer aided design of solvent blends for hybrid cooling and antisolvent crystallization of active pharmaceutical ingredients

Choosing a solvent and an antisolvent for a new crystallization process is challenging due to the sheer number of possible solvent mixtures and the impact of solvent composition and crystallization temperature on process performance. To facilitate this choice, we present a general computer aided mixture/blend design (CAMbD) formulation for the design of optimal solvent mixtures for the crystallization of pharmaceutical products. The proposed methodology enables the simultaneous identification of the optimal process temperature, solvent, antisolvent, and composition of solvent mixture. The SAFT-γ Mie group-contribution approach is used in the design of crystallization solvents; based on an equilibrium model, both the crystal yield and solvent consumption are considered. The design formulation is implemented in gPROMS and applied to the crystallization of lovastatin and ibuprofen, where a hybrid approach combining cooling and antisolvent crystallization is compared to each method alone. For lovastatin, the use of a hybrid approach leads to an increase in crystal yield compared to antisolvent crystallization or cooling crystallization. Furthermore, it is seen that using less volatile but powerful crystallization solvents at lower temperatures can lead to better performance. When considering ibuprofen, the hybrid and antisolvent crystallization techniques provide a similar performance, but the use of solvent mixtures throughout the crystallization is critical in maximizing crystal yields and minimizing solvent consumption. We show that our more general approach to rational design of solvent blends brings significant benefits for the design of crystallization processes in pharmaceutical and chemical manufacturing

Metodología integrada para el diseño de productos incluyendo criterios de sostenibilidad y los requerimientos de la cadena de valor del aceite de palma

ilustraciones a color, diagramas, fotografíasThe decision-making process for the design of chemical products is a key activity to increase its acceptance in the market and to enhance its sustainability performance. For this, it is important to consider not only the preferences of the consumers but also the requirements of the supply chain, which are not taken into account in existing design methodologies. Therefore, this study presents a methodology to support the product design process considering the requirements of the supply chain. The methodology is implemented in 2 phases: a diagnostic phase of the supply chain where different stakeholders are interviewed to know their limitations; and a product design phase, where a workshop is developed including the identified limitations in the design problem. The methodology was tested in a case study: the design of chemical products from the palm oil supply chain. Palm oil is the highest vegetable oil productivity but it is in the spotlight because of its implications for environmental and social issues. The diagnostic phase involved the participation of 8 different stakeholders of the palm oil supply chain who were interviewed individually through semi-structured interviews of one hour. The information was systematically analyzed using qualitative methods. The second phase design workshop was conducted with three focus groups, two with chemical engineering students and one with expert product designers from a Latin American food company. The groups were asked to select between different surfactants (some of them based on palm oil) to create a vegetable milk drink; at first, without considering the limitations of the supply chain, and at a second time, considering them. The results of the first phase showed that the main requirements of the supply chain according to the interviewed stakeholders are the last of integration between the actors of the supply chain (overall in relation to the last stages when final products are developed), the dependency to the oil suppliers and the possible biodiesel/food competition, and the supply chain's lack of capacity to adapt to changes. When this information was used for the product design workshop, the two groups of students modified their design decision when they considered the supply chain limitations. By their part, experienced designers did not modify their design, but they found that this type of analysis could be relevant for other products, for example those as the margarines with have a high oil content. (Texto tomado de la fuente)El proceso de toma de decisiones para el diseño de productos químicos es una actividad clave para aumentar su aceptación en el mercado y mejorar su rendimiento en materia de sostenibilidad. Para ello, es importante tener en cuenta no sólo las preferencias de los consumidores, sino también los requisitos de la cadena de suministro, que no se tienen en cuenta en las metodologías de diseño existentes. Por lo tanto, este estudio presenta una metodología para apoyar el proceso de diseño de productos teniendo en cuenta los requisitos de la cadena de suministro. La metodología se implementa en 2 fases: una fase de diagnóstico de la cadena de suministro, en la que se entrevista a las diferentes partes interesadas para conocer sus limitaciones; y una fase de diseño del producto, en la que se desarrolla un taller que incluye las limitaciones identificadas en el problema de diseño. La metodología se puso a prueba en un estudio de caso: el diseño de productos químicos a partir de la cadena de suministro del aceite de palma. El aceite de palma es el aceite vegetal de mayor productividad, pero está en el punto de mira por sus implicaciones en cuestiones medioambientales y sociales. La fase de diagnóstico contó con la participación de 8 partes interesadas diferentes de la cadena de suministro del aceite de palma. Fueron entrevistadas individualmente mediante entrevistas semiestructuradas de una hora de duración. La información se analizó sistemáticamente utilizando métodos cualitativos. La segunda fase del taller de diseño se llevó a cabo con tres grupos focales, dos con estudiantes de ingeniería química y uno con diseñadores de productos expertos de una empresa latinoamericana de productos alimenticios. Se pidió a los grupos que seleccionaran entre distintos tensioactivos (algunos de ellos a base de aceite de palma) para crear una bebida no láctea de origen vegetal; en un primer momento, sin tener en cuenta las limitaciones de la cadena de suministro, y en un segundo, teniéndolas en cuenta. Los resultados de la primera fase mostraron que los principales requisitos de la cadena de suministro según las partes interesadas entrevistadas son la falta de integración entre los actores de la cadena de suministro (sobre todo en relación con las últimas fases en las que se desarrollan los productos finales), la dependencia de los proveedores de aceite y la posible competencia entre biodiésel y alimentos, y la falta de capacidad de la cadena de suministro para adaptarse a los cambios. Cuando se utilizó esta información para el taller de diseño del producto, los dos grupos de estudiantes modificaron su decisión de diseño al considerar las limitaciones de la cadena de suministro. Por su parte, los diseñadores experimentados no modificaron su diseño, pero descubrieron que este tipo de análisis podría ser relevante para otros productos, por ejemplo, aquellos como las margarinas con alto contenido en aceite.MaestríaMagíster en Ingeniería - Ingeniería QuímicaGrupo de Investigación en Procesos Químicos y Bioquímico