Sustainability of the chemical manufacturing industry - Towards a new paradigm?

This paper describes the current situation of the chemicalmanufacturingindustry, with special reference to Europe and looks to the future sustainability demands on the sector, and the implications of these demands for chemical engineering education. These implications include definitions of sustainability criteria for the sector and the need for transparent reporting under the Triple Bottom Line approach. The response of the education system to the sustainability agenda over the years and a number of strategies to incorporate it into courses are described. The important role of chemical (or more generally, process) engineers in delivering sustainable solutions is emphasised but this also suggests that anew way of thinking about the discipline is required. Indeed, this paper argues that the demand for a sustainable chemicalmanufacturing sector could bring about the next paradigm shift in the discipline which has been predicted for some time

Entropy: The Markov Ordering Approach

The focus of this article is on entropy and Markov processes. We study the properties of functionals which are invariant with respect to monotonic transformations and analyze two invariant "additivity" properties: (i) existence of a monotonic transformation which makes the functional additive with respect to the joining of independent systems and (ii) existence of a monotonic transformation which makes the functional additive with respect to the partitioning of the space of states. All Lyapunov functionals for Markov chains which have properties (i) and (ii) are derived. We describe the most general ordering of the distribution space, with respect to which all continuous-time Markov processes are monotonic (the {\em Markov order}). The solution differs significantly from the ordering given by the inequality of entropy growth. For inference, this approach results in a convex compact set of conditionally "most random" distributions.Comment: 50 pages, 4 figures, Postprint version. More detailed discussion of the various entropy additivity properties and separation of variables for independent subsystems in MaxEnt problem is added in Section 4.2. Bibliography is extende

Computer aided framework for designing bio-based commodity molecules with enhanced properties

We investigate the use of computer aided molecular design (CAMD) approach for enhancing the properties of existing molecules by modifying their chemical structure to match target property values. The activity of tailoring molecules requires to aggregate knowledge disseminated across the whole chemical enterprise hierarchy, from the manager level to the chemists and chemical engineers, with different backgrounds and perception of what the ideal molecule would be. So, we propose a framework that allows the search to be successful in matching all requirements while capitalizing this knowledge spread among actors with different backgrounds with the help of SBVR (Semantics of Business Vocabulary and Rules) and OCL (Object Constraint Language). In the context of using biomass as the feedstock, we discuss the coupling of CAMD tools with computer aided organic synthesis tools so as to propose enhanced bio-sourced molecule candidates which could be synthesized with eco-friendly pathways. Finally, we evaluate the sustainability of the molecules and of the whole decision-process as well. Specific applications that concern the use of bio-sourced molecules are presented: a case of typical derivatives of chemical platform molecules issued from the itaconic acid to substitute N-methyl-2-pyrrolidone NMP or dimethyl-formamide DMF solvents and a case of derivatives of lipids to be used a biolubricants

Ternary Blends of Vegetable Oils: Thermal Profile Predictions for Product Design

This work deals with Product Design by means of theoretical predictions of the Solid Fat Content of different formulations using 3 vegetable oils. A Soli-Liquid Equilibrium (SLE) model was implemented and integrated into an optimization algorithm based on the Generalized Reduced Gradient method. A total of 3,696 SLE problems are solved, covering 57 binary blends, 3 pure vegetable oils and 171 ternary blends problems, before and after chemical interesterification reaction and at 8 different temperatures. A combinatorial random distribution of fatty acids in the glycerol structure is used to simulate the effect of the reaction. The results were compared with 256 experimental points, giving an average absolute error of 5.4 and 4.4 in Solid Fat Content for systems before and after reaction, respectively. Computer-aided tools can be useful to deal with the large combinatorial problem faced by product design, especially when desired product performance is related to a phase behavior in multicomponent mixtures

Chemical enterprise model and decision-making framework for sustainable chemical product design

The chemical product substitution process is undertaken by chemical industries for complying with regulations, like REACH in Europe. Initially devoted to chemists, chemicals substitution is nowadays a complex process involving corporate, business and engineering stakeholders across the chemical enterprise for orienting the search toward a sustainable solution. We formalize a decision making process framework dedicated to the sustainable chemical product design activity in an industrial context. The framework aims at improving the sharing of information and knowledge and at enabling a collaborative work across the chemical enterprise stakeholders at the strategic, tactical and operational levels. It is supported by information and communication technologies (ICT) and integrates a computer aided molecular design tool. During the initial intelligence phase, a systemic analysis of the needs and usages enables to define the product requirements. In the design phase, they are compiled with the help of a facilitator to generate the input file of a computer aided product design tool. This multiobjective tool is designed to find mixtures with molecular fragments issued from renewable raw materials, and is able to handle environment-health and safety related properties along with process physicochemical properties. The final choice phase discusses the solution relevancy and provides feedback, before launching the product manufacturing. The framework is illustrated by the search of a bio-sourced water–solvent mixture formulation for lithographic blanket wash used in printing industry. The sustainability of the solution is assessed by using the sustainability shades metho

Thermodynamic Tree: The Space of Admissible Paths

Is a spontaneous transition from a state x to a state y allowed by thermodynamics? Such a question arises often in chemical thermodynamics and kinetics. We ask the more formal question: is there a continuous path between these states, along which the conservation laws hold, the concentrations remain non-negative and the relevant thermodynamic potential G (Gibbs energy, for example) monotonically decreases? The obvious necessary condition, G(x)\geq G(y), is not sufficient, and we construct the necessary and sufficient conditions. For example, it is impossible to overstep the equilibrium in 1-dimensional (1D) systems (with n components and n-1 conservation laws). The system cannot come from a state x to a state y if they are on the opposite sides of the equilibrium even if G(x) > G(y). We find the general multidimensional analogue of this 1D rule and constructively solve the problem of the thermodynamically admissible transitions. We study dynamical systems, which are given in a positively invariant convex polyhedron D and have a convex Lyapunov function G. An admissible path is a continuous curve along which $G$ does not increase. For x,y from D, x\geq y (x precedes y) if there exists an admissible path from x to y and x \sim y if x\geq y and y\geq x. The tree of G in D is a quotient space D/~. We provide an algorithm for the construction of this tree. In this algorithm, the restriction of G onto the 1-skeleton of $D$ (the union of edges) is used. The problem of existence of admissible paths between states is solved constructively. The regions attainable by the admissible paths are described.Comment: Extended version, 31 page, 9 figures, 69 cited references, many minor correction

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

Microencapsulation of thyme oil by coacervation : production, characterization and release evaluation

Tese de Doutoramento. Engenharia Química e Biológica. Faculdade de Engenharia. Universidade do Porto. 201