Soorathep Kheawhom

An ellipsoidal off-line model predictive control strategy for linear parameter varying systems with applications in chemical processes

Abstract In this paper, a novel off-line model predictive control strategy for linear parameter varying systems is presented. The on-line computational burdens are reduced by pre-computing off-line the sequences of state feedback gains corresponding to the sequences of nested ellipsoids. The number of sequences of nested ellipsoids constructed is equal to the number of vertices of the polytope. At each sampling instant, the smallest ellipsoid containing the currently measured state is determined in each sequence of ellipsoids and the scheduling parameter is measured. The real-time state feedback gain is calculated by linear interpolation between the corresponding state feedback gains. An overall algorithm is proved to guarantee robust stability. The controller design is illustrated with two examples of continuous stirred tank reactors.

Comparison of Reactive Inkjet Printing and Reactive Sintering to Fabricate Metal Conductive Patterns

Two methods to fabricate metal conductive patterns including reactive inkjet printing and reactive sintering were investigated. The silver printed lines were prepared from reactive inkjet printing of silver nitrate and L-ascorbic acid. Alternatively, the silver lines were prepared by the reactive sintering process of ethylene glycol vapor at 250 °C and formic acid vapor at 150 °C. In reactive printing, we investigated the effect of the number of printing cycles and the effect of silver nitrate concentration on the properties of the conductive patterns obtained. In reactive sintering, we investigated the usage of formic acid and ethylene glycol as reducing agents. The effect of reactive sintering time on the properties of the conductive patterns obtained was studied. As compared to reactive inkjet printing, the reactive sintering process gives more smooth and contiguous pattern resulting in lower resistivity. The resistivity of the silver line obtained by ethylene glycol vapor reduction at 250 °C for 30 min was 12 µΩ cm, which is about eight times higher than that of bulk silver. In contrast, the copper lines were fabricated by reactive inkjet printing and reactive sintering using various conditions of formic acid, ethylene glycol and hydrogen atmosphere, the copper lines printed have no conductivity due to the formation of copper oxide.

Characterization of copper–zinc nanoparticles synthesized via submerged arc discharge with successive reduction process

Two copper–zinc (Cu–Zn) alloy wires with different copper contents (90Cu/10Zn and 65Cu/35Zn) were used as sacrificed electrodes in submerged arc discharge process at ambient pressure. Three types of dielectric liquids including ethylene glycol, ethanol and deionized water were investigated. The microstructure, composition and size of the particles obtained were examined by high-resolution transmission electron microscopy with energy dispersive X-ray spectroscopy (TEM/EDS) and X-ray diffraction (XRD) analysis. The particles synthesized from both wires in all dielectric liquids were spherical with size in the range of 5–50 nm. Ethylene glycol yielded the smallest particles, in comparison deionized water yielded the largest particles. These particles contained Cu, Cu–Zn, and zinc oxide without copper oxide. l-Ascorbic acid was then used as a reducing agent to eliminate zinc oxide. Thus, the 5–50 nm spherical Cu/Cu–Zn nanoparticles with similar composition to the wires used, can be synthesized. However, ethanol cannot be used as dielectric liquid in case of 65Cu/35Zn wire, because high zinc content in ethanol with l-ascorbic acid formed zinc oxalate. Conductive ink was prepared from the particles synthesized by using 90Cu/10Zn in ethylene glycol, and screen printed on poly(ethylene terephthalate) (PET) substrate. The patterns printed could be sintered in air at 150 °C for 60 min. The patterns fabricated were characterized by scanning electron microscope with energy dispersive X-ray spectroscopy (SEM/EDS). The resistivity of the conductive pattern was measured by two-point probe at room temperature. The lowest volume resistivity of the pattern obtained was 125 µΩcm.

An interpolation-based robust MPC algorithm using polyhedral invariant sets

This article presents an interpolation-based robust MPC algorithm for uncertain polytopic discrete-time systems using polyhedral invariant sets. Two nested polyhedral invariant sets are constructed off-line by solving robust constrained model predictive control optimization problems. The first one is a large set constructed to cover all of the desired operating spaces. The second one is a small target set constructed to drive the terminal state into. The real-time control law is calculated by linear interpolation between the two state feedback gains corresponding to these nested precomputed polyhedral invariant sets. At each sampling instant, only a computationally low-demanding optimization problem is needed to be solved on-line. The controller design is illustrated with an example. The proposed algorithm can achieve good control performance while on-line computation is still tractable.

An Off-Line Formulation of Tube-Based Robust MPC Using Polyhedral Invariant Sets

In this paper, an off-line formulation of tube-based robust model predictive control (MPC) using polyhedral invariant sets is proposed. A novel feature is the fact that no optimal control problem needs to be solved at each sampling time. Moreover, the proposed tube-based robust MPC algorithm can deal with the linear time-varying (LTV) system with bounded disturbance. The simulation results show that the state at each time step is restricted to lie within a tube whose center is the state of the nominal LTV system that converges to the origin. Finally, the state is kept within a tube whose center is at the origin, so robust stability is guaranteed. Satisfaction of the state and control constraints is guaranteed by employing tighter constraint sets for the nominal LTV system.

Performance Analysis of a Biomass Supercritical Water Gasification Process under Energy Self-sufficient Condition

Abstract Depletion of fossil fuel and environmental concerns stimulate the use of clean and renewable energy. Biomass is regarded as a potential energy source and can be efficiently converted to a useful synthesis gas via incomplete combustion in a gasification process. However, the thermal gasification causes a tar formation and requires high energy input when wet biomass is used. The objective of this study is to investigate the performance of an autothermal biomass gasification process in supercritical water. A flowsheet model of the gasification process is developed and validated with experimental data. Thermodynamic analysis is performed based on the minimization of total Gibbs free energy. Simulations are performed to study effects of key operational parameters on the supercritical water gasification process at an energy self-sufficient condition, which a total net heat energy can be zero. Hydrogen in the synthesis gas product and thermal efficiency of the gasification process are also considered and suitable operating conditions of the autothermal biomass gasification for hydrogen production are identified.

Simultaneous estimation of thin film thickness and optical properties using two-stage optimization

In this work, we proposed the new method for estimation of the thickness and the optical properties of the thin metal oxide film deposited on a transparent substrate. The developed method uses only transmittance spectra measured. Our method is based on the two stage optimization where the thickness is determined in the outer stage and the optical properties are determined in the inner stage. The differential evolutionary algorithm is used in solving the formulated problem. The proposed method was illustrated in the case study of Titanium dioxide film deposited on a glass substrate. The results indicate that the thickness and the optical properties estimated agree well with the experiment. Moreover, we investigated robustness of the proposed method in the case of transmittance spectra containing noises. The data were modelled by adding random noises ranging between 0 and 30% to the transmittance spectra measured. It is seen that the proposed method has better robustness and performance than the existing method based on pointwise unconstrained minimization approach. In solving the estimation problem, the performance of the proposed method was also compared with the well-known Levenberg–Marquardt method and single stage differential evolutionary method. The results indicate that the proposed method has better performance than Levenberg–Marquardt method and single stage differential evolutionary method. Moreover, the proposed method is more robust to random noise than Levenberg–Marquardt method and single stage differential evolutionary method.

Ethanol as an electrolyte additive for alkaline zinc-air flow batteries

Zinc-air flow batteries exhibit high energy density and offer several appealing advantages. However, their low efficiency of zinc utilization resulted from passivation and corrosion of the zinc anodes has limited their broad application. In this work, ethanol, which is considered as an environmentally friendly solvent, is examined as an electrolyte additive to potassium hydroxide (KOH) aqueous electrolyte to improve electrochemical performance of the batteries. Besides, the effects of adding different percentages of ethanol (0–50% v/v) to 8 M KOH aqueous electrolyte were investigated and discussed. Cyclic voltammograms revealed that the presence of 5–10% v/v ethanol is attributed to the enhancement of zinc dissolution and the hindrance of zinc anode passivation. Also, potentiodynamic polarization and electrochemical impedance spectroscopy confirmed that adding 5–10% v/v ethanol could effectively suppress the formation of passivating layers on the active surface of the zinc anodes. Though the addition of ethanol increased solution resistance and hence slightly decreased the discharge potential of the batteries, a significant enhancement of discharge capacity and energy density could be sought. Also, galvanostatic discharge results indicated that the battery using 10% v/v ethanol electrolyte exhibited the highest electrochemical performance with 30% increase in discharge capacity and 16% increase in specific energy over that of KOH electrolyte without ethanol.

Robust Model Predictive Control Strategy for LTV and LPV Systems of the Internal Reforming Solid Oxide Fuel Cell

Abstract Solid oxide fuel cell (SOFC) is an electrochemical device operating at a high temperature, converting the chemical energy of a fuel directly to electrical energy. Moreover, it can directly convert hydrocarbon fuels to a hydrogen-rich gas via internal reforming inside the fuel cell stack itself. However, the endothermic cooling effect resulting from the reforming reaction at the anode side causes the temperature gradient and thermal stress within the fuel cell stack. This requires an efficient control design for assuring a stability of the system. In this study, a robust linear model predictive control (MPC) based on uncertain polytopic approach is synthesized for controlling the SOFC. Different designs of the robust MPC using linear time-varying (LTV) and linear parameter varying (LPV) models are studied. The state feedback control laws are derived by minimizing an upper bound on the worst-case performance cost and are implemented to the cell voltage and temperature controls of the direct internal reforming SOFC. The simulation results show that under model uncertainties, the proposed robust MPC can control the SOFC when disturbances in the fuel feed and air temperature are introduced and guarantee the stability of the SOFC. The performance of the MPC using different linear models is compared and discussed.

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.