Evolutionary optimization of a fed-batch penicillin fermentation process

This paper presents a genetic algorithms approach for the optimization of a fed-batch penicillin fermentation process. A customized float-encoding genetic algorithm is developed and implemented to a benchmark fed-batch penicillin fermentation process. Off-line optimization of the initial conditions and set points are carried out in two stages for a single variable and multiple variables. Further investigations with online optimization have been carried out to demonstrate that the yield can be significantly improved with an optimal feed rate profile. The results have shown that the proposed approaches can be successfully applied to optimization problems of fed-batch fermentation to improve the operation of such processes

Economic and environmental impacts of the energy source for the utility production system in the HDA process

The well-known benchmark process for hydrodealkylation of toluene (HDA) to produce benzene is revisited in a multi-objective approach for identifying environmentally friendly and cost-effective operation solutions. The paper begins with the presentation of the numerical tools used in this work, i.e., a multi-objective genetic algorithm and a Multiple Choice Decision Making procedure. Then, two studies related to the energy source involved in the utility production system (UPS), either fuel oil or natural gas, of the HDA process are carried out. In each case, a multi-objective optimization problem based on the minimization of the total annual cost of the process and of five environmental burdens, that are Global Warming Potential, Acidification Potential, Photochemical Ozone Creation Potential, Human Toxicity Potential and Eutrophication Potential, is solved and the best solution is identified by use of Multiple Choice Decision Making procedures. An assessment of the respective contribution of the HDA process and the UPS towards environmental impacts on the one hand, and of the environmental impacts generated by the main equipment items of the HDA process on the other hand is then performed to compare both solutions. This ‘‘gate-to-gate’’ environmental study is then enlarged by implementing a ‘‘cradle-togate’’ Life Cycle Assessment (LCA), for accounting of emission inventory and extraction. The use of a natural gas turbine, less economically efficient, turns out to be a more attractive alternative to meet the societal expectations concerning environment preservation and sustainable development

Closure in artificial cell signalling networks - investigating the emergence of cognition in collectively autocatalytic reaction networks

Cell Signalling Networks (CSNs) are complex biochemical networks responsible for the coordination of cellular activities in response to internal and external stimuli. We hypothesize that CSNs are subsets of collectively autocatalytic reaction networks. The signal processing or cognitive abilities of CSNs would originate from the closure properties of these systems. We investigate how Artificial CSNs, regarded as minimal cognitive systems, could emerge and evolve under this condition where closure may interact with evolution. To assist this research, we employ a multi-level concurrent Artificial Chemistry based on the Molecular Classifier Systems and the Holland broadcast language. A critical issue for the evolvability of such undirected and autonomous evolutionary systems is to identify the conditions that would ensure evolutionary stability. In this paper we present some key features of our system which permitted stable cooperation to occur between the different molecular species through evolution. Following this, we present an experiment in which we evolved a simple closed reaction network to accomplish a pre-specified task. In this experiment we show that the signal-processing ability (signal amplification) directly resulted from the evolved systems closure properties

Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels

Developing data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problemPeer ReviewedPostprint (author's final draft

Off-the-rack instead of tailor-made module-based plant design at equipment level

Module-based plant design facilitates a paradigm shift in chemical and biochemical industry to decrease the time needed for plant design. Instead of a tailored design of apparatuses for a target production rate, modules are selected off-the-rack to set up a production plant. Within the scope of this thesis, four important areas of module-based plant design at equipment level are investigated. First, the determination of a plants’ overall operating window, a prerequisite for equipment module selection and evaluation is improved by considering the so far neglected non-linear dependency between the operating constraints and the production rate of a plant. Second, the currently accepted view that investment costs are determining the decision on the use of equipment modules for different process units is disproved and novel preselection approaches are proposed, applied and evaluated. A preselection approach based on investment and operating costs is rated most suitable to decide on the use of equipment modules for a case study. The third area explored is equipment module selection for a constant market demand, aiming at flexibility in production rate at low investment costs, as well as for a market demand development. It is shown by case studies that modular production plants offer a promising alternative to conventionally designed plants. Finally, an approach to design equipment modules for flexibility in production rate is introduced and applied. For the case study of a heat exchanger it is shown that a four times larger operating window can be obtained at only 14 % higher total annual costs compared to a conventionally designed heat exchanger. Hence, this work investigates four key areas in module-based plant design at equipment level beyond current state of the art contributing to a paradigm shift in plant design.Modulbasierte Anlagenplanung ermöglicht einen Paradigmenwechsel in der chemischen und biochemischen Industrie, um die Zeit der Anlagenplanung zu verkürzen. Anstelle einer maßgeschneiderten Auslegung von Apparaten für einen Auslegungspunkt werden Module von der Stange ausgewählt, um eine Produktionsanlage zu errichten. Im Rahmen dieser Arbeit werden vier wichtige Bereiche der modulbasierten Anlagenplanung auf Equipmentebene untersucht. Erstens wird die Bestimmung des Gesamtbetriebsfensters einer Anlage, eine Voraussetzung für die Auswahl von Equipmentmodulen und Bewertung von modularen Anlagen, durch die Berücksichtigung der bisher vernachlässigten und nichtlinearen Abhängigkeit zwischen den Betriebsgrenzen und der Produktionsrate einer Anlage verbessert. Zweitens werden aktuelle Entscheidungskriterien für den Einsatz von Equipmentmodulen für verschiedene Prozesseinheiten in Frage gestellt und neue Vorauswahlmethoden vorgeschlagen, angewendet und bewertet. Dabei wird die derzeit akzeptierte Ansicht, dass Investitionskosten bestimmend sind, widerlegt. Eine Vorauswahlmethode, um über die Verwendung von Equipmentmodulen zu entscheiden, die auf Investitions- und Betriebskosten basiert, wird für eine Fallstudie als am geeignetsten bewertet. Der dritte untersuchte Bereich behandelt die Auswahl von Equipmentmodulen für eine konstante Marktnachfrage, mit dem Ziel einer hohen Flexibilität in der Produktionsrate bei niedrigen Investitionskosten, sowie für eine Marktnachfrageentwicklung. Anhand von Fallstudien wird gezeigt, dass modulare Produktionsanlagen eine vielversprechende Alternative zu konventionell ausgelegten Anlagen darstellen. Abschließend wird ein Ansatz zur Auslegung von Equipmentmodulen für eine hohe Flexibilität in der Produktionsrate vorgestellt und angewendet. Am Beispiel eines Wärmeübertragers wird gezeigt, dass ein viermal größeres Betriebsfenster für nur 14 % höhere jährliche Gesamtkosten im Vergleich zu einem konventionell ausgelegten Wärmeübertrager erreicht werden kann. Somit untersucht diese Arbeit vier wichtige Bereiche der modulbasierten Anlagenplanung auf Equipmentebene über den aktuellen Stand der Technik hinaus und liefert ihren Beitrag für einen Paradigmenwechsel in der Anlagenplanung

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

Identifying Vulnerabilities of Industrial Control Systems using Evolutionary Multiobjective Optimisation

In this paper we propose a novel methodology to assist in identifying vulnerabilities in a real-world complex heterogeneous industrial control systems (ICS) using two evolutionary multiobjective optimisation (EMO) algorithms, NSGA-II and SPEA2. Our approach is evaluated on a well known benchmark chemical plant simulator, the Tennessee Eastman (TE) process model. We identified vulnerabilities in individual components of the TE model and then made use of these to generate combinatorial attacks to damage the safety of the system, and to cause economic loss. Results were compared against random attacks, and the performance of the EMO algorithms were evaluated using hypervolume, spread and inverted generational distance (IGD) metrics. A defence against these attacks in the form of a novel intrusion detection system was developed, using a number of machine learning algorithms. Designed approach was further tested against the developed detection methods. Results demonstrate that EMO algorithms are a promising tool in the identification of the most vulnerable components of ICS, and weaknesses of any existing detection systems in place to protect the system. The proposed approach can be used by control and security engineers to design security aware control, and test the effectiveness of security mechanisms, both during design, and later during system operation.Comment: 25 page