Analysis of Advanced Process Control Technology and Economic Assessment Improvement

Advanced Process Control (APC) is considered for investment after the Distributed Control System (DCS) and Historian System of Industrial Automation and Control Systems (IACS) had been implemented. The benefits of APC application can be observed by economic assessment (EA), however the EA technique is still behind the development of APC technology. We review the literature on APC and EA and highlight the potential future development

Iterative Learning Control of Energy Management System: Survey on Multi-Agent System Framework

This paper presents a brief survey of recent works on Iterative Learning Control (ILC) of Energy Management System (EMS) based on a framework of Multi-Agent System (MAS). ILC is a control methodology which is especially suitable for dynamical systems whose control tasks are executed in a finite time interval and are repeated over and over. The key idea of ILC is to take available system information in the past and current runs, to generate the control input for the next run. EMS is a computer-based system to monitor energy consumption, control operation, and optimize energy supplies and demands. EMS can be naturally modeled as MAS since each power-generated or power-consumed component of EMS can be cast as agent. Each agent of MAS is a dynamical system itself and has its own target such as tracking desired trajectory and minimizing energy. Moreover, there are common objectives of EMS which aim to attain its energy efficiency, reliability and optimality. Then one agent can cooperate with other agents to achieve some global objectives, in addition to their own local goals, by exchanging information with other agents. Lastly, we will explore some open research problems and their potential applications

Analysis of Multi-objective Optimal Dispatch of Cogeneration with Thermal Energy Storage for Building Energy Management System

This paper presents analysis of the multi-objective optimal operation of designed BEMS which contains cogeneration or combined heat and power (CHP) and thermal energy storage (TES) as energy sources. The previously designed BEMS consists of CHP as the main energy supply with absorption chiller and auxiliary boiler. It is observed that there is excessive heat energy from CHP operation which is enough for further utilization. In this paper, TES is additional component to utilize excessive heat energy released from CHP operation. TES cooperates with CHP and auxiliary boiler to supply heat energy to meet the cooling load demand in the building. There are two objective functions for consideration, namely, total operating cost (TOC) and total carbon dioxide emission (TCOE). The multi-objective framework combines both objective functions and employs the weighted sum of TOC and TCOE. Furthermore, we vary initial state of TES from 0 - 20% of TES’s capacity and analyze its effect on TOC and TCOE. We apply the multi-objective approach to a large shopping mall. Numerical results show that setting initial state of TES to 0% can offer more reduction of TOC and TCOE than other initial conditions. The multi-objective optimal operation converges to minimum TOC when a weighting factor is 0. On the other hand, it converges to the minimum TCOE when the weighting factor is 1. In addition, the trade-off curve showing a relationship between TOC and TCOE provides operating points which depends on operator’s decision criterion

On Computing the Worst-case H∞ Performance of Lur'e Systems with Uncertain Time-invariant Delays

This paper presents a worst-case H∞ performance analysis for Lur'e systems with time-invariant delays. The sucient condition to guarantee an upper bound of worst-case performance is developed based on the delay-partitioning Lyapunov-Krasovskii functional containing the integral of sector-bounded nonlinearities. Using Jensen inequality and S-procedure, the delay-dependent criterion is given in terms of linear matrix inequalities. In addition, we extend the criterion to compute the worst-case performance for Lur'e systems subject to norm-bounded uncertainties by using a matrix eliminating lemma. Numerical results show that our criterion provide the least upper bound on the worst-case H∞ performance comparing to the criteria derived based on existing techniques

A Review of Chaos Control Strategies for Tri-trophic Food Chain Ecological Systems

The existence of chaos in ecological models is quite obvious due to the presence of nonlinear terms. Controlling chaotic phenomena in ecological systems remains a difficult task due to their unpredictability, and thus chaos control is one of the main objectives for constructing mathematical models in ecology today. Our aim in this paper is to review chaos control strategies for the tri-trophic food chain models by using various ecological factors. The factors include additional food, prey refuge, the Allee effect, the fear effect, and harvesting. We establish the essential conditions for the existence of ecologically feasible equilibrium points in the food chain ecological systems and their local stability. This paper provides a unified overview of recent research on the chaos control of ecological systems. The theoretical results suggest a way to control populations of species in ecological systems for fishing and pest management in farming. Numerical examples are performed to justify and compare the theoretical findings through phase portraits and bifurcation diagrams

Economic Assessment of APC and RTO Using Option to Expand

This paper aims to develop a new economic assessment (EA) method using the option to expand for Advanced Process Control (APC) and Real Time Optimization (RTO). The new EA criteria for investment decision for APC and RTO employ net present value of APC and call option of RTO. Calculation of call option adapts arithmetic measurement method to compute annualized volatility. The new EA applies scenario analysis to take appropriate action. There are four scenarios and their corresponding actions, namely, (1) safe scenario - invest only APC, (2) value-added scenario and (3) risky scenario - invest in APC and RTO, (4) gamble scenario - reject APC. Furthermore, early exercise criterion for RTO investment uses American option method. Applying new EA method to VCM plant demonstrates the effectiveness of the option to expand. The results show that when NPV of APC is negative and the sum of NPV of APC and Call of RTO is positive, APC project is risky scenario. We recommend to invest in APC and RTO. In comparison to conventional NPV and Payback Period (PB) methods, APC is not feasible since NPV is negative and PB is not available due to negative expected profit. In the case study, volatility calculation addresses only one product line in chemical industry which is VCM. Real production comprises of multiple product lines and their volatility is larger than that of one product. With the new EA method, management has comprehensive and flexible tool to assess APC/RTO benefits. Moreover, the new EA provides the timing to invest RTO. Profit margin, expiration period and yield are key parameters that affect early exercise. The new EA is the first method to apply real options to APC and RTO which evaluates the benefit not only APC but also the integrated APC and RTO. The early exercise criterion can facilitate the decision maker to invest in the most beneficial period

Enhancement of Investment Decision Making Using Real Options with Application to Advanced Process Control Project

Feasibility study is one of the most important parts of decision making for project investment. Current industrial approach to estimate benefit of advanced process control (APC) is based on the conventional estimation techniques, namely, statistical analysis and payback period. These conventional approaches can answer the investment either 'Go' or 'No Go'. The gap analysis reveals that economics uncertainties and inflexibility of decision criteria are the issues required improvement on the decision making process. In this paper, we apply a real options approach to develop option to defer analysis as part of the proposed feasibility study of APC project. We demonstrate improvement of the proposed method with a case study on ethylene plant in Thailand. The result shows that option to defer can additionally answer 'when to defer' and 'when to invest'. Hence, this approach enhances the investment decision making under economics uncertainties and provides flexible decision criteria

Consensus Synthesis of Robust Cooperative Control for Homogeneous Leader-Follower Multi-Agent Systems Subject to Parametric Uncertainty

This paper presents a design of robust consensus for homogeneous leader-follower multiagent systems (MAS). Each agent of MAS is described by a linear time-invariant dynamic model subject to parametric uncertainty. The agents are interconnected through a static interconnection matrix over an undirected graph to cooperate and share information with their neighbours. The consensus design of MAS can be transformed to stability analysis by using decomposition technique. We apply Lyapunov theorem to derive the sufficient condition to ensure the consensus of all independent subsystems. In addition, we design a robust distributed state feedback gain based on linear quadratic regulator (LQR) setting. Controller gain is computed via solving a linear matrix inequality. As a result, we provide a robust design procedure of a cooperative LQR control to achieve consensus objective and maximize the admissible bound of the uncertainty. Finally, we give numerical examples to illustrate the effectiveness of the proposed consensus design. The results show that the response for MAS in presence of uncertainty using robust consensus design follows the response of the leader and is better than that of the existingnominal consensus design

Convex Optimization Approach to Multi-Objective Design of Two-Stage Compensators for Linear Systems

The previous design of two-stage compensators of linear systems was focused on the stabilization and low sensitivity. However, it has not considered the time-domain performance of the closed-loop system, especially, reference tracking. This paper aims to propose the design method of the two-stage compensators that additionally achieves good transient response. Applying Q-parameterization to the standard control system can formulate the two-stage compensator design as a convex optimization problem. The infinite dimensional problem is transformed into a finite dimensional problem by Ritz approximation. Finally, the convex optimization is efficiently solved to give the optimal controller. The numerical results show that the proposed design method on the second order benchmark problem and the first order plus time delay system improves the time-domain performance while the closed-loop system is stable and the influence of disturbance to output is attenuated