A Polyhedral Off-Line Robust MPC Strategy for Uncertain Polytopic Discrete-Time Systems

In this paper, an off-line synthesis approach to robust constrained model predictive control for uncertain polytopic discrete-time systems is presented. Most of the computational burdens are moved off-line by pre-computing a sequence of state feedback control laws that corresponds to a sequence of polyhedral invariant sets. The state feedback control laws computed are derived by minimizing the nominal performance cost in order to improve control performance. At each sampling instant, the smallest polyhedral invariant set containing the currently measured state is determined. The corresponding state feedback control law is then implemented to the process. The controller design is illustrated with two examples in chemical processes. The proposed algorithm is compared with an ellipsoidal off-line robust model predictive control algorithm derived by minimizing the worst-case performance cost and an ellipsoidal off-line robust model predictive control algorithm derived by minimizing the nominal performance cost. The results show that the proposed algorithm can achieve better control performance. Moreover, a significantly larger stabilizable region is obtained

Interpolation-based Off-line Robust MPC for Uncertain Polytopic Discrete-time Systems

In this paper, interpolation-based off-line robust MPC for uncertain polytopic discrete-time systems is presented. Instead of solving an on-line optimization problem at each sampling time to find a state feedback gain, a sequence of state feedback gains is pre-computed off-line in order to reduce the on-line computational time. At each sampling time, the real-time state feedback gain is calculated by linear interpolation between the pre-computed state feedback gains. Three interpolation techniques are proposed. In the first technique, the smallest ellipsoids containing the measured state are approximated and the corresponding real-time state feedback gain is calculated. In the second technique, the pre-computed state feedback gains are interpolated in order to get the largest possible real-time state feedback gain while robust stability is still guaranteed. In the last technique, the real-time state feedback gain is calculated by minimizing the violation of the constraints of the adjacent inner ellipsoids so the real-time state feedback gain calculated has to regulate the state from the current ellipsoids to the adjacent inner ellipsoids as fast as possible. As compared to on-line robust MPC, the proposed techniques can significantly reduce on-line computational time while the same level of control performance is still ensured

MPC for LPV Systems Based on Parameter-Dependent Lyapunov Function with Perturbation on Control Input Strategy

In this paper, the model predictive control (MPC) algorithm for linear parameter varying (LPV) systems is proposed. The proposed algorithm consists of two steps. The first step is derived by using parameter-dependent Lyapunov function and the second step is derived by using the perturbation on control input strategy. In order to achieve good control performance, the bounds on the rate of variation of the parameters are taken into account in the controller synthesis. An overall algorithm is proved to guarantee robust stability. The controller design is illustrated with two case studies of continuous stirred-tank reactors. Comparisons with other MPC algorithms for LPV systems have been undertaken. The results show that the proposed algorithm can achieve better control performance

Optimal Dosing of Breast Cancer Chemotherapy Using Robust MPC Based on Linear Matrix Inequalities

In this paper, we consider an application of robust model predictive control to optimal dosing of breast cancer chemotherapy. The model-patient mismatch is handled by computing an ellipsoidal invariant set containing the measured patient's states at each sampling time. An optimal dose of chemotherapeutic agent is obtained by solving a convex optimization problem subject to linear matrix inequalities. In the case study of simulated patients, the results show that the tumor volume can be reduced to a specified target with up to 30% model-patient mismatch. Moreover, the robust model predictive control algorithm can achieve better treatment results as compared with the nonlinear model predictive control algorithm while the on-line computational time is significantly reduced

A PDE-Based Data Reconciliation Approach for Systems with Variations of Parameters

Data reconciliation is a mathematical approach that improves the quality of measurements by calculating the reconciled data that satisfies the process constraints. The conventional data reconciliation approach relies on the process model that contains the constant parameters. In the industrial applications, however, there are always possible variations of parameters within the system. In this paper, a new data reconciliation approach based on the partial differential equation (PDE) is developed. The proposed data reconciliation approach is experimentally applied to a case study of temperature measurements for a refinery process. The PDE-based model is employed in the formulation of the optimization problem. Unlike the conventional data reconciliation approach in which the system is assumed to be lumped, the PDE-based data reconciliation approach includes in the problem formulation the variations of parameters within the system in order to describe the real system’s behaviour. The reconciled values can be computed within the computational domain so they can be used as the data for troubleshooting, equipment analysis and maintenance

Electronic and Ionic Conductivities Enhancement of Zinc Anode for Flexible Printed Zinc-Air Battery

Zinc-air battery is considered a promising candidate for future energy applications due to its high energy density, safety and low cost. However, poor battery performance and low efficiency of zinc utilization, resulted from passivation effect of the zinc anode, is a major challenge. Thus, in this work, investigation of electronic and ionic conductivities enhancement of the zinc anode for flexible printed zinc-air battery has been carried out. The anode was made from a zinc-based ink, prepared from a mixture of zinc and zinc oxide particles. Carbon black, sodium silicate (Na2SiO3) and bismuth oxide (Bi2O3) were investigated for implementation on the anode. The results showed that performance of the battery increased when carbon black was introduced into the anode as the presence of carbon black improved electronic conductivity of the anode. Again, the battery performed better when Bi2O3 orNa2SiO3 was introduced due to the formation of solid electrolyte interface (SEI) on the anode. The SEI inhibits passivation of zinc active surfaces and provides effective electrolyte access. The battery with Bi2O3 provided the best performance. The highest performance was observed when Bi2O3 content reached 26wt.%. No significant improvement was observed whenBi2O3 concentration increased higher than 26 wt.%.Zinc-air battery is considered a promising candidate for future energy applications due to its high energy density, safety and low cost. However, poor battery performance and low efficiency of zinc utilization, resulted from passivation effect of the zinc anode, is a major challenge. Thus, in this work, investigation of electronic and ionic conductivities enhancement of the zinc anode for flexible printed zinc-air battery has been carried out. The anode was made from a zinc-based ink, prepared from a mixture of zinc and zinc oxide particles. Carbon black, sodium silicate (Na2SiO3) and bismuth oxide (Bi2O3) were investigated for implementation on the anode. The results showed that performance of the battery increased when carbon black was introduced into the anode as the presence of carbon black improved electronic conductivity of the anode. Again, the battery performed better when Bi2O3 or Na2SiO3 was introduced due to the formation of solid electrolyte interface (SEI) on the anode. The SEI inhibits passivation of zinc active surfaces and provides effective electrolyte access. The battery with Bi2O3 provided the best performance. The highest performance was observed when Bi2O3 content reached 26 wt.%. No significant improvement was observed when Bi2O3 concentration increased higher than 26 wt.%

Development of a Data-Driven Soft Sensor for Multivariate Chemical Processes Using Concordance Correlation Coefficient Subsets Integrated with Parallel Inverse-Free Extreme Learning Machine

Nonlinearity, complexity, and technological limitations are causes of troublesome measurements in multivariate chemical processes. In order to deal with these problems, a soft sensor based on concordance correlation coefficient subsets integrated with parallel inverse-free extreme learning machine (CCCS-PIFELM) is proposed for multivariate chemical processes. In comparison to the forward propagation architecture of neural network with a single hidden layer, i.e., a traditional extreme learning machine (ELM), the CCCS-PIFELM approach has two notable points. Firstly, there are two subsets obtained through the concordance correlation coefficient (CCC) values between input and output variables. Hence, impacts of input variables on output variables can be assessed. Secondly, an inverse-free algorithm is used to reduce the computational load. In the evaluation of the prediction performance, the Tennessee Eastman (TE) benchmark process is employed as a case study to develop the CCCS-PIFELM approach for predicting product compositions. According to the simulation results, the proposed CCCS-PIFELM approach can obtain higher prediction accuracy compared to traditional approaches

Off-Line Robust Constrained MPC for Linear Time-Varying Systems with Persistent Disturbances

An off-line robust constrained model predictive control (MPC) algorithm for linear time-varying (LTV) systems is developed. A novel feature is the fact that both model uncertainty and bounded additive disturbance are explicitly taken into account in the off-line formulation of MPC. In order to reduce the on-line computational burdens, a sequence of explicit control laws corresponding to a sequence of positively invariant sets is computed off-line. At each sampling time, the smallest positively invariant set containing the measured state is determined and the corresponding control law is implemented in the process. The proposed MPC algorithm can guarantee robust stability while ensuring the satisfaction of input and output constraints. The effectiveness of the proposed MPC algorithm is illustrated by two examples

Simulation of steam gasification in a fluidized bed reactor with energy self-sufficient condition

YesThe biomass gasification process is widely accepted as a popular technology to produce fuel for the application in gas turbines and Organic Rankine Cycle (ORC). Chemical reactions of this process can be separated into three reaction zones: pyrolysis, combustion, and reduction. In this study, sensitivity analysis with respect to three input parameters (gasification temperature, equivalence ratio, and steam-to-biomass ratio) has been carried out to achieve energy self-sufficient conditions in a steam gasification process under the criteria that the carbon conversion efficiency must be more than 70%, and carbon dioxide gas is lower than 20%. Simulation models of the steam gasification process have been carried out by ASPEN Plus and validated with both experimental data and simulation results from Nikoo & Mahinpey (2008). Gasification temperature of 911 °C, equivalence ratio of 0.18, and a steam-to-biomass ratio of 1.78, are considered as an optimal operation point to achieve energy self-sufficient condition. This operating point gives the maximum of carbon conversion efficiency at 91.03%, and carbon dioxide gas at 15.18 volumetric percentages. In this study, life cycle assessment (LCA) is included to compare the environmental performance of conventional and energy self-sufficient gasification for steam biomass gasification.Financing of this research was supported by the Thailand Research Fund (TRF) under Grant Number PHD57I0054 and the Institutional Research Grant by the Thailand Research Fund (TRF) under Grant Number IRG 5780014 and Chulalongkorn University, Contact No. RES_57_411_21_076

Model-Based Analysis of an Integrated Zinc-Air Flow Battery/Zinc Electrolyzer System

This work aims at analyzing an integrated system of a zinc-air flow battery with a zinc electrolyzer for energy storage application. For efficient utilization of inherently intermittent renewable energy sources, safe and cost-effective energy storage systems are required. A zinc-air flow battery integrated with a zinc electrolyzer shows great promise as an electricity storage system due to its high specific energy density at low cost. A mathematical model of the system was developed. The model was implemented in MATLAB and validated against experimental results. The validation of the model was verified by the agreement between the simulation and experimental polarization characteristic. The behavior and performance of the system were then examined as a function of different operating parameters: the flow rate of the electrolyte, the initial concentration of potassium hydroxide (KOH) and the initial concentration of zincate ion. These parameters significantly affected the performance of the system. The influence of the hydrogen evolution reaction (HER) on the performance of the system was investigated and discussed as it significantly affected the coulombic efficiencies of both the zinc-air flow battery and the zinc electrolyzer. Optimal KOH concentration was found to be about 6–7 M. Whilst increased KOH concentration enhanced the discharge energy of the battery, it also increased HER of both the battery and the electrolyzer. However, higher initial concentration of zincate ion reduced HER and improved the coulombic efficiency of the system. Besides, a higher flow rate of electrolyte enhanced the performance of the system especially at a high charge/discharge current by maintaining the concentration of active species in the cell. Nevertheless, the battery suffered from a higher rate of HER at a high flow rate. It was noted that the model-based analysis provided better insight into the behavioral characteristics of the system leading to an improved design and operation of the integrated system of zinc-air flow battery with the zinc electrolyzer