Safe model-based design of experiments using Gaussian processes

The construction of kinetic models has become an indispensable step in developing and scale-up of processes in the industry. Model-based design of experiments (MBDoE) has been widely used to improve parameter precision in nonlinear dynamic systems. Such a framework needs to account for both parametric and structural uncertainty, as the physical or safety constraints imposed on the system may well turn out to be violated, leading to unsafe experimental conditions when an optimally designed experiment is performed. In this work, Gaussian processes are utilized in a two-fold manner: 1) to quantify the uncertainty realization of the physical system and calculate the plant-model mismatch, 2) to compute the optimal experimental design while accounting for the parametric uncertainty. TheOur proposed method, Gaussian process-based MBDoE (GP-MBDoE), guarantees the probabilistic satisfaction of the constraints in the context of the model-based design of experiments. GP-MBDoE is assisted with the use of adaptive trust regions to facilitate a satisfactory local approximation. The proposed method can allow the design of optimal experiments starting from limited preliminary knowledge of the parameter set, leading to a safe exploration of the parameter space. This method’s performance is demonstrated through illustrative case studies regarding the parameter identification of kinetic models in flow reactors

Safe real-time optimization using multi-fidelity guassian processes

This paper proposes a new class of real-time optimization schemes to overcome system-model mismatch of uncertain processes. This work's novelty lies in integrating derivative-free optimization schemes and multi-fidelity Gaussian processes within a Bayesian optimization framework. The proposed scheme uses two Gaussian processes for the stochastic system, one emulates the (known) process model, and another, the true system through measurements. In this way, low fidelity samples can be obtained via a model, while high fidelity samples are obtained through measurements of the system. This framework captures the system's behavior in a non-parametric fashion while driving exploration through acquisition functions. The benefit of using a Gaussian process to represent the system is the ability to perform uncertainty quantification in real-time and allow for chance constraints to be satisfied with high confidence. This results in a practical approach that is illustrated in numerical case studies, including a semi-batch photobioreactor optimization problem

Stability Analysis of Piecewise Affine Systems with Multi-model Model Predictive Control

Constrained model predictive control (MPC) is a widely used control strategy, which employs moving horizon-based on-line optimisation to compute the optimum path of the manipulated variables. Nonlinear MPC can utilize detailed models but it is computationally expensive; on the other hand linear MPC may not be adequate. Piecewise affine (PWA) models can describe the underlying nonlinear dynamics more accurately, therefore they can provide a viable trade-off through their use in multi-model linear MPC configurations, which avoid integer programming. However, such schemes may introduce uncertainty affecting the closed loop stability. In this work, we propose an input to output stability analysis for closed loop systems, consisting of PWA models, where an observer and multi-model linear MPC are applied together, under unstructured uncertainty. Integral quadratic constraints (IQCs) are employed to assess the robustness of MPC under uncertainty. We create a model pool, by performing linearisation on selected transient points. All the possible uncertainties and nonlinearities (including the controller) can be introduced in the framework, assuming that they admit the appropriate IQCs, whilst the dissipation inequality can provide necessary conditions incorporating IQCs. We demonstrate the existence of static multipliers, which can reduce the conservatism of the stability analysis significantly. The proposed methodology is demonstrated through two engineering case studies.Comment: 28 pages 9 figure

Simultaneous Process Design and Control Optimization using Reinforcement Learning

With the ever-increasing numbers in population and quality in healthcare, it is inevitable for the demand of energy and natural resources to rise. Therefore, it is important to design highly efficient and sustainable chemical processes in the pursuit of sustainability. The performance of a chemical plant is highly affected by its design and control. A design cannot be evaluated without its controls and vice versa. To optimally address design and control simultaneously, one must formulate a bi-level mixed-integer nonlinear program with a dynamic optimization problem as the inner problem; this, is intractable. However, by computing an optimal policy using reinforcement learning, a controller with close-form expression can be found and embedded into the mathematical program. In this work, an approach using a policy gradient method along with mathematical programming to solve the problem simultaneously is proposed. The approach was tested in two case studies and the performance of the controller was evaluated. It was shown that the proposed approach outperforms current state-of-the-art control strategies. This opens a whole new range of possibilities to address the simultaneous design and control of engineering systems

Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization

Model‐based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low‐quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics‐based and data‐driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high‐quality data by correcting raw process measurements via a physics‐based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data‐driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re‐fitting the simple kinetic model (soft sensor) using the data‐driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed‐batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open‐loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application

Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty

Dynamic real-time optimization (DRTO) is a challenging task due to the fact that optimal operating conditions must be computed in real time. The main bottleneck in the industrial application of DRTO is the presence of uncertainty. Many stochastic systems present the following obstacles: 1) plant-model mismatch, 2) process disturbances, 3) risks in violation of process constraints. To accommodate these difficulties, we present a constrained reinforcement learning (RL) based approach. RL naturally handles the process uncertainty by computing an optimal feedback policy. However, no state constraints can be introduced intuitively. To address this problem, we present a chance-constrained RL methodology. We use chance constraints to guarantee the probabilistic satisfaction of process constraints, which is accomplished by introducing backoffs, such that the optimal policy and backoffs are computed simultaneously. Backoffs are adjusted using the empirical cumulative distribution function to guarantee the satisfaction of a joint chance constraint. The advantage and performance of this strategy are illustrated through a stochastic dynamic bioprocess optimization problem, to produce sustainable high-value bioproducts

On the anomalous optical conductivity dispersion of electrically conducting polymers: Ultra-wide spectral range ellipsometry combined with a Drude-Lorentz model

Electrically conducting polymers (ECPs) are becoming increasingly important in areas such as optoelectronics, biomedical devices, and energy systems. Still, their detailed charge transport properties produce an anomalous optical conductivity dispersion that is not yet fully understood in terms of physical model equations for the broad range optical response. Several modifications to the classical Drude model have been proposed to account for a strong non-Drude behavior from terahertz (THz) to infrared (IR) ranges, typically by implementing negative amplitude oscillator functions to the model dielectric function that effectively reduce the conductivity in those ranges. Here we present an alternative description that modifies the Drude model via addition of positive-amplitude Lorentz oscillator functions. We evaluate this so-called Drude-Lorentz (DL) model based on the first ultra-wide spectral range ellipsometry study of ECPs, spanning over four orders of magnitude: from 0.41 meV in the THz range to 5.90 eV in the ultraviolet range, using thin films of poly(3,4-ethylenedioxythiophene):tosylate (PEDOT:Tos) as a model system. The model could accurately fit the experimental data in the whole ultrawide spectral range and provide the complex anisotropic optical conductivity of the material. Examining the resonance frequencies and widths of the Lorentz oscillators reveals that both spectrally narrow vibrational resonances and broader resonances due to localization processes contribute significantly to the deviation from the Drude optical conductivity dispersion. As verified by independent electrical measurements, the DL model accurately determines the electrical properties of the thin film, including DC conductivity, charge density, and (anisotropic) mobility. The ellipsometric method combined with the DL model may thereby become an effective and reliable tool in determining both optical and electrical properties of ECPs, indicating its future potential as a contact-free alternative to traditional electrical characterization. © The Royal Society of Chemistry 2019

On the anomalous optical conductivity dispersion of electrically conducting polymers: Ultra-wide spectral range ellipsometry combined with a Drude-Lorentz model

Electrically conducting polymers (ECPs) are becoming increasingly important in areas such as optoelectronics, biomedical devices, and energy systems. Still, their detailed charge transport properties produce an anomalous optical conductivity dispersion that is not yet fully understood in terms of physical model equations for the broad range optical response. Several modifications to the classical Drude model have been proposed to account for a strong non-Drude behavior from terahertz (THz) to infrared (IR) ranges, typically by implementing negative amplitude oscillator functions to the model dielectric function that effectively reduce the conductivity in those ranges. Here we present an alternative description that modifies the Drude model via addition of positive-amplitude Lorentz oscillator functions. We evaluate this so-called Drude-Lorentz (DL) model based on the first ultra-wide spectral range ellipsometry study of ECPs, spanning over four orders of magnitude: from 0.41 meV in the THz range to 5.90 eV in the ultraviolet range, using thin films of poly(3,4-ethylenedioxythiophene):tosylate (PEDOT:Tos) as a model system. The model could accurately fit the experimental data in the whole ultrawide spectral range and provide the complex anisotropic optical conductivity of the material. Examining the resonance frequencies and widths of the Lorentz oscillators reveals that both spectrally narrow vibrational resonances and broader resonances due to localization processes contribute significantly to the deviation from the Drude optical conductivity dispersion. As verified by independent electrical measurements, the DL model accurately determines the electrical properties of the thin film, including DC conductivity, charge density, and (anisotropic) mobility. The ellipsometric method combined with the DL model may thereby become an effective and reliable tool in determining both optical and electrical properties of ECPs, indicating its future potential as a contact-free alternative to traditional electrical characterization. © The Royal Society of Chemistry 2019