A hierarchical Mamdani-type fuzzy modelling approach with new training data selection and multi-objective optimisation mechanisms: A special application for the prediction of mechanical properties of alloy steels

In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows to construct Mamdani fuzzy models considering both accuracy (precision) and transparency (interpretability) of fuzzy systems. The new methodology employs a fast hierarchical clustering algorithm to generate an initial fuzzy model efficiently; a training data selection mechanism is developed to identify appropriate and efficient data as learning samples; a high-performance Particle Swarm Optimisation (PSO) based multi-objective optimisation mechanism is developed to further improve the fuzzy model in terms of both the structure and the parameters; and a new tolerance analysis method is proposed to derive the confidence bands relating to the final elicited models. This proposed modelling approach is evaluated using two benchmark problems and is shown to outperform other modelling approaches. Furthermore, the proposed approach is successfully applied to complex high-dimensional modelling problems for manufacturing of alloy steels, using ‘real’ industrial data. These problems concern the prediction of the mechanical properties of alloy steels by correlating them with the heat treatment process conditions as well as the weight percentages of the chemical compositions

QM/MM Simulation

Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have become a popular tool for investigating chemical reactions in condensed phases. In QM/MM methods, the region of the system in which the chemical process takes place is treated at an appropriate level of quantum chemistry theory, while the remainder is described by a molecular mechanics force field. Within this approach, chemical reactivity can be studied in large systems, such as enzymes. Using quality management to calculate the properties of a large chemical library is time-consuming and costly. High-precision QM/MM calculation is a multi-scale calculation method to study ligand binding. By using quantum chemistry representing ligands combined with molecular mechanics to characterize proteins and solvents, modelling time can be greatly reduced. Our bioinformaticians will provide you the most efficient quantum chemistry services

On the stochastic modelling of surface reactions through reflected chemical Langevin equations

Modelling of small-scale heterogeneous catalytic systems with master equations captures the impact of molecular noise, but can be computationally expensive. On the other hand, the chemical Fokker-Planck approximation offers an excellent alternative from an efficiency perspective. The Langevin equation can generate stochastic realisations of the Fokker-Planck equation; yet, these realisations may violate the conditions 0 <= θ <= 1 (where θ is surface coverage). In this work, we adopt Skorokhod’s formulations to impose reflective boundaries that remedy this issue. We demonstrate the approach on a simple system involving a single species and describing adsorption, desorption, reaction and diffusion processes on a lattice. We compare different numerical schemes for the solution of the resulting reflected Langevin equation and calculate rates of convergence. Our benchmarks should guide the choice of appropriate numerical methods for the accurate and efficient simulation of chemical systems in the catalysis field

The development of a weighted directed graph model for dynamic systems and application of Dijkstra’s algorithm to solve optimal control problems.

Master of Science (Chemical Engineering). University of KwaZulu-Natal. Durban, 2017.Optimal control problems are frequently encountered in chemical engineering process control applications as a result of the drive for more regulatory compliant, efficient and economical operation of chemical processes. Despite the significant advancements that have been made in Optimal Control Theory and the development of methods to solve this class of optimization problems, limitations in their applicability to non-linear systems inherent in chemical process unit operations still remains a challenge, particularly in determining a globally optimal solution and solutions to systems that contain state constraints. The objective of this thesis was to develop a method for modelling a chemical process based dynamic system as a graph so that an optimal control problem based on the system can be solved as a shortest path graph search problem by applying Dijkstra’s Algorithm. Dijkstra’s algorithm was selected as it is proven to be a robust and global optimal solution based algorithm for solving the shortest path graph search problem in various applications. In the developed approach, the chemical process dynamic system was modelled as a weighted directed graph and the continuous optimal control problem was reformulated as graph search problem by applying appropriate finite discretization and graph theoretic modelling techniques. The objective functional and constraints of an optimal control problem were successfully incorporated into the developed weighted directed graph model and the graph was optimized to represent the optimal transitions between the states of the dynamic system, resulting in an Optimal State Transition Graph (OST Graph). The optimal control solution for shifting the system from an initial state to every other achievable state for the dynamic system was determined by applying Dijkstra’s Algorithm to the OST Graph. The developed OST Graph-Dijkstra’s Algorithm optimal control solution approach successfully solved optimal control problems for a linear nuclear reactor system, a non-linear jacketed continuous stirred tank reactor system and a non-linear non-adiabatic batch reactor system. The optimal control solutions obtained by the developed approach were compared with solutions obtained by the variational calculus, Iterative Dynamic Programming and the globally optimal value-iteration based Dynamic Programming optimal control solution approaches. Results revealed that the developed OST Graph-Dijkstra’s Algorithm approach provided a 14.74% improvement in the optimality of the optimal control solution compared to the variational calculus solution approach, a 0.39% improvement compared to the Iterative Dynamic Programming approach and the exact same solution as the value–iteration Dynamic Programming approach. The computational runtimes for optimal control solutions determined by the OST Graph-Dijkstra’s Algorithm approach were 1 hr 58 min 33.19 s for the nuclear reactor system, 2 min 25.81s for the jacketed reactor system and 8.91s for the batch reactor system. It was concluded from this work that the proposed method is a promising approach for solving optimal control problems for chemical process-based dynamic systems

Escaping the trap of complication and complexity in multiscale microkinetic modelling of heterogeneous catalytic processes

In this feature article, the development of methods to enable a hierarchical multiscale approach to the microkinetic analysis of heterogeneous catalytic processes is reviewed. This methodology is an effective route to escape the trap of complication and complexity in multiscale microkinetic modelling. On the one hand, the complication of the problem is related to the fact that the observed catalyst functionality is inherently a multiscale property of the reacting system and its analysis requires bridging the phenomena at different time and length scales. On the other hand, the complexity of the problem derives from the system dimension of the chemical systems, which typically results in a number of elementary steps and species, that are beyond the limit of accessibility of present-day computational power even for the most efficient implementation of atomistic first-principles simulations. The main idea behind the hierarchical approach is to tackle the problem with methods of increasing accuracy in a dual feed-back loop between theory and experiments. The potential of the methodology is shown in the context of unravelling the WGS and r-WGS catalytic mechanisms on Rh catalysts. As a perspective, the extension to structure-dependent microkinetic modelling is discussed

Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

Economic and environmental strategies for process design

This paper first addresses the definition of various objectives involved in eco-efficient processes, taking simultaneously into account ecological and economic considerations. The environmental aspect at the preliminary design phase of chemical processes is quantified by using a set of metrics or indicators following the guidelines of sustainability concepts proposed by . The resulting multiobjective problem is solved by a genetic algorithm following an improved variant of the so-called NSGA II algorithm. A key point for evaluating environmental burdens is the use of the package ARIANE™, a decision support tool dedicated to the management of plants utilities (steam, electricity, hot water, etc.) and pollutants (CO2, SO2, NO, etc.), implemented here both to compute the primary energy requirements of the process and to quantify its pollutant emissions. The well-known benchmark process for hydrodealkylation (HDA) of toluene to produce benzene, revisited here in a multiobjective optimization way, is used to illustrate the approach for finding eco-friendly and cost-effective designs. Preliminary biobjective studies are carried out for eliminating redundant environmental objectives. The trade-off between economic and environmental objectives is illustrated through Pareto curves. In order to aid decision making among the various alternatives that can be generated after this step, a synthetic evaluation method, based on the so-called Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) (), has been first used. Another simple procedure named FUCA has also been implemented and shown its efficiency vs. TOPSIS. Two scenarios are studied; in the former, the goal is to find the best trade-off between economic and ecological aspects while the latter case aims at defining the best compromise between economic and more strict environmental impact

The Polarizable Continuum Model Goes Viral! Extensible, Modular and Sustainable Development of Quantum Mechanical Continuum Solvation Models

Synergistic theoretical and experimental approaches to challenging chemical problems have become more and more widespread, due to the availability of efficient and accurate ab initio quantum chemical models. Limitations to such an approach do, however, still exist. The vast majority of chemical phenomena happens in complex environments, where the molecule of interest can interact with a large number of other moieties, solvent molecules or residues in a protein. These systems represent an ongoing challenge to our modelling capabilities, especially when high accuracy is required for the prediction of exotic and novel molecular properties. How to achieve the insight needed to understand and predict the physics and chemistry of such complex systems is still an open question. I will present our efforts in answering this question based on the development of the polarizable continuum model for solvation. While the solute is described by a quantum mechanical method, the surrounding environment is replaced by a structureless continuum dielectric. The mutual polarization of the solute-environment system is described by classical electrostatics. Despite its inherent simplifications, the model contains the basic mathematical features of more refined explicit quantum/classical polarizable models. Leveraging this fundamental similarity, we show how the inclusion of environment effects for relativistic and nonrelativistic quantum mechanical Hamiltonians, arbitrary order response properties and high-level electron correlation methods can be transparently derived and implemented. The computer implementation of the polarizable continuum model is central to the work presented in this dissertation. The quantum chemistry software ecosystem suffers from a growing complexity. Modular programming offers an extensible, flexible and sustainable paradigm to implement new features with reduced effort. PCMSolver, our open-source application programming interface, can provide continuum solvation functionality to any quantum chemistry software: continuum solvation goes viral. Our strategy affords simpler programming workflows, more thorough testing and lower overall code complexity. As examples of the flexibility of our implementation approach, we present results for the continuum modelling of non homogeneous environments and how wavelet-based numerical methods greatly outperform the accuracy of traditional methods usually employed in continuum solvation models

Modelling and simulation framework for reactive transport of organic contaminants in bed-sediments using a pure java object - oriented paradigm

Numerical modelling and simulation of organic contaminant reactive transport in the environment is being increasingly relied upon for a wide range of tasks associated with risk-based decision-making, such as prediction of contaminant profiles, optimisation of remediation methods, and monitoring of changes resulting from an implemented remediation scheme. The lack of integration of multiple mechanistic models to a single modelling framework, however, has prevented the field of reactive transport modelling in bed-sediments from developing a cohesive understanding of contaminant fate and behaviour in the aquatic sediment environment. This paper will investigate the problems involved in the model integration process, discuss modelling and software development approaches, and present preliminary results from use of CORETRANS, a predictive modelling framework that simulates 1-dimensional organic contaminant reaction and transport in bed-sediments