Nonlinear Dynamic Analysis and Control of Chemical Processes Using Dynamic Operability

Nonlinear dynamic analysis serves an increasingly important role in process systems engineering research. Understanding the nonlinear dynamics from the mathematical model of a process helps to find the boundaries of all achievable process conditions and identify the system instabilities. The information on such boundaries is beneficial for optimizing the design and formulating a control structure. However, a systematic approach to analyzing nonlinear dynamics of chemical processes considering such boundaries in a quantifiable and adaptable way is yet to exist in the literature. The primary aim of this work is to formulate theoretical concepts for dynamic operability, as well as develop the practical implementation methods for the analysis of dynamic performance in chemical processes. Process operability is a powerful tool for analyzing the relationships between the input variables, the output variables, and the disturbances via the geometric computation of variable sets. The operability sets are described by unions of polyhedra, which can be translated to sets of inequality constraints, so the results of the operability analysis can be used for process optimization and advanced process control. Nonetheless, existing process operability approaches in the literature are currently limited for steady-state processes and a generalized definition of dynamic operability that retains the core principles of steady-state operability as a controllability measure. A unified dynamic operability concept is proposed in this dissertation with two different adaptations to represent the complex relationships between the design, control structure, and control law of a given process. The existing operability mapping methods discretize the input space by partitioning the ranges of each input variable evenly, and all possible input combinations are simulated to achieve the output sets. The procedure is repeated for each value in the expected disturbance set to find the output regions that are guaranteed to be achieved regardless of the disturbance scenario. However, for dynamic systems, the same set of manipulated inputs can take different values at different time intervals, so the number of possible input combinations, which is also the number of simulations required, increases exponentially with the number of time intervals. This tractability challenge motivates the development of novel dynamic operability mapping approaches. A linear time-invariant dynamic system is first considered to tackle the dynamic mapping of achievable output sets. For a linear system, the achievable output set (AOS) at a fixed predicted time is the smallest convex hull that contains all the images of the extreme points of the available input set (AIS) when propagated through the dynamic model. Given a collection of AOS’s at all predicted times, referred to as the achievable funnel, a set of output constraints is infeasible if its intersection with the achievable funnel is empty. Under the influence of a stochastic disturbance, the achievable funnel is shifted according to the definition of the expected disturbance set (EDS). If the EDS is bounded, the intersection of all achievable funnels at each disturbance realization is the tightest set of transient output constraints that is operable. Additionally, given a fixed setpoint, an AOS is referred to as a feasible AOS if a series of inputs from the AIS always brings any output to the setpoint regardless of the realization of the disturbance within the EDS. Thus, novel developed theories and algorithms to update the dynamic operability mapping according to the current state variables and the disturbance propagations are proposed to reduce the online computational time of the constraint calculation task. Dynamic operability mapping for nonlinear processes is an expansion of the above linear mapping. A novel state-space projection mapping is proposed by taking advantage of the discrete-time state-space structure of the dynamic model to reduce the number of input mapping combinations. This method augments the AIS at the current step to include the AOS of the state variables from the previous time step. The nonlinear dynamic operability mapping framework consists of three components: the AOS inspector, the AIS divider, and the merger of the AOS from the previous time with the AIS. Specifically, the AOS inspector evaluates if the current input-output combinations are approximately accurate to the real AOS when all input combinations are mapped to the output space. If the AOS inspector gauges that the current AOS is not sufficiently precise, the AIS divider systematically generates more input-output combinations based on the current AOS. This feedback process is repeated until an accuracy tolerance is reached. Finally, a novel grey-box model identification algorithm for process control is developed by integrating dynamic operability mapping and Bayesian calibration. The proposed dynamic discrepancy reduced-order model-based approach calibrates the rates of changes of the grey-box model to match the plant instead of compensating for the time-varying output differences. The model reduction framework is divided into three steps: formulating the dynamic discrepancy terms, calibrating the hyperparameters, and selecting the least complex model that is neither underfitted nor overfitted. To demonstrate the effectiveness of the reduced-order model, the developed approach is implemented into a model predictive controller for a high-fidelity model as the simulated plant

When management encounters complexity

This paper aims at showing how management has come to encounter the sciences of complexity. Therefore the various levels and domains of management are outlined which leverage from the study of complexity. This is not, however, a descriptive study. Rather, we focus on how management can benefit from knowing of the sciences of complexity. New tools and rods, new languages and approaches are sketched that show a radical shift in management leading from a once dependent discipline from physics and engineering, towards a biologically and ecologically permeated new management.Whereas the main concern for complexity consists in understanding complex phenomena and systems, at the end a number of successful applications of complexity to management and entrepreneurial consulting are considered

Overview of Multiobjective Optimization Methods in in Silico Metabolic Engineering

Multiobjective optimization requires of finding a trade-off between multiple objectives. However, most of the objectives are contradict towards each other, thus makes it difficult for the traditional approaches to find a solution that satisfies all objectives. Fortunately, the problems are able to solve by the aid of Pareto methods. Meanwhile, in in silico Metabolic Engineering, the identification of reaction knockout strategies that produce mutant strains with a permissible growth rate and product rate of desired metabolites is still hindered. Previously, Evolutionary Algorithms (EAs) has been successfully used in determining the reaction knockout strategies. Nevertheless, most methods work by optimizing one objective function, which is growth rate or production rate. Furthermore, in bioprocesses, it involves multiple and conflicting objectives. In this review, we aim to show the different multiobjective evolutionary optimization methods developed for tackling the multiple and conflicting objectives in in silico metabolic engineering, as well as the approaches in multiobjective optimization

Evolutionary Dynamic Multi-Objective Optimisation : A survey

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Unified control/structure design and modeling research

To demonstrate the applicability of the control theory for distributed systems to large flexible space structures, research was focused on a model of a space antenna which consists of a rigid hub, flexible ribs, and a mesh reflecting surface. The space antenna model used is discussed along with the finite element approximation of the distributed model. The basic control problem is to design an optimal or near-optimal compensator to suppress the linear vibrations and rigid-body displacements of the structure. The application of an infinite dimensional Linear Quadratic Gaussian (LQG) control theory to flexible structure is discussed. Two basic approaches for robustness enhancement were investigated: loop transfer recovery and sensitivity optimization. A third approach synthesized from elements of these two basic approaches is currently under development. The control driven finite element approximation of flexible structures is discussed. Three sets of finite element basic vectors for computing functional control gains are compared. The possibility of constructing a finite element scheme to approximate the infinite dimensional Hamiltonian system directly, instead of indirectly is discussed

A model-based control concept for a demand-driven biogas production

With the expansion of highly fluctuating renewable energies (like wind power and photovoltaics) in the last few years, the intelligent integration of these new energy sources into the German energy system is becoming one of the central challenges. Biogas plants can play a key role in this transition. The present thesis investigates the possibilities, underlying mechanisms and dependencies establishing a flexible biogas production by means of demand-driven feeding. Furthermore, a robust control concept for demand-driven operation has to be developed and demonstrated in full-scale.Mit dem Ausbau von fluktuierenden erneuerbaren Energien (Windkraft, Photovoltaik) und dem voraussichtlichen Weiterschreiten dieser Entwicklung wird die intelligente Integration dieser Energiequellen in das Energiesystem zur zentralen Herausforderung. Biogasanlagen besitzen dabei eine Schlüsselrolle. Die vorliegende Dissertation untersucht die Möglichkeiten, zugrundeliegende Mechanismen und Abhängigkeiten zur Etablierung einer flexiblen Biogasproduktion durch bedarfsgesteuerte Fütterung. Es ist ein robustes Regelungskonzept entwickelt und im großtechnischen Maßstab demonstriert worden

Design of an Optimal Fractional Complex Order PID Controller for Buck Converter

Dynamic and robust controllers are the inherent requirement of power electronic converters, which are subjected to dynamic variations and nonlinearities. The effectiveness of fractional order controllers in non-linear system control has been well-established by studies in the past few decades. Various forms of fractional order controllers have been used in power-electronic control. Recent research indicates that complex order controllers, extensions of fractional controllers, are more robust against uncertainties and non-linearities than their integer and fractional order counterparts. Though complex order controllers have been employed in various nonlinear plants, they have not been extensively tested on power electronic applications. Also, the design and tuning of the controller is difficult. This paper investigates the effectiveness of a complex order PID controller on a typical power electronic DC-DC buck converter for the first time. Two types of complex order controllers of the form PI^{a+ib}D^c and PI^{a+ib}D^{c+id} were designed for a power electronic buck converter. The complex order controllers were implemented in Simulink and the optimal tuning of the complex order controller parameters for various performance indices was performed using different optimization algorithms. The Cohort Intelligence algorithm was found to give the most optimal results. Both the complex controllers showed more robustness towards uncertainties than the linear and fractional PID controllers. The PI^{a+ib}D^c controller gave the smoothest and fastest response under non-linearities. The dynamic performance of the complex order controller is the best and can be expected to be useful for more power electronic applications

plant-wide control of industrial processes using rigorous simulation and heuristics

Ph.DDOCTOR OF PHILOSOPH

Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey

Metaheuristics have been acknowledged as an effective solution for many difficult issues related to optimization. The metaheuristics, especially swarm’s intelligence and evolutionary computing algorithms, have gained popularity within a short time over the past two decades. Various metaheuristics algorithms are being introduced on an annual basis and applications that are more new are gradually being discovered. This paper presents a survey for the years 2011-2021 on multiple metaheuristics algorithms, particularly swarm and evolutionary algorithms, to identify a nonlinear block-oriented model called the Hammerstein model, mainly because such model has garnered much interest amidst researchers to identify nonlinear systems. Besides introducing a complete survey on the various population-based algorithms to identify the Hammerstein model, this paper also investigated some empirically verified actual process plants results. As such, this article serves as a guideline on the fundamentals of identifying nonlinear block-oriented models for new practitioners, apart from presenting a comprehensive summary of cutting-edge trends within the context of this topic area