Structured Mathematical Modeling of Industrial Boiler

As a major utility system in industry, boilers consume a large portion of the total energy and costs. Significant reduction of boiler cost operation can be gained through improvements in efficiency. In accomplishing such a goal, an adequate dynamic model that comprehensively reflects boiler characteristics is required. This paper outlines the idea of developing a mathematical model of a water-tube industrial boiler based on first principles guided by the bond graph method in its derivation. The model describes the temperature dynamics of the boiler subsystems such as economizer, steam drum, desuperheater, and superheater. The mathematical model was examined using industrial boiler performance test data.It can be used to build a boiler simulator or help operators run a boiler effectively

Modeling and supervisory control design for a combined cycle power plant

The traditional control strategy based on PID controllers may be unsatisfactory when dealing with processes with large time delay and constraints. This paper presents a supervisory model based constrained predictive controller (MPC) for a combined cycle power plant (CCPP). First, a non-linear dynamic model of CCPP using the laws of physics was proposed. Then, the supervisory control using the linear constrained MPC method was designed to tune the performance of the PID controllers by including output constraints and manipulating the set points. This scheme showed excellent tracking and disturbance rejection results and improved performance compared with a stand-alone PID controller’s scheme

Distributed model predictive control of steam/water loop in large scale ships

In modern steam power plants, the ever-increasing complexity requires great reliability and flexibility of the control system. Hence, in this paper, the feasibility of a distributed model predictive control (DiMPC) strategy with an extended prediction self-adaptive control (EPSAC) framework is studied, in which the multiple controllers allow each sub-loop to have its own requirement flexibility. Meanwhile, the model predictive control can guarantee a good performance for the system with constraints. The performance is compared against a decentralized model predictive control (DeMPC) and a centralized model predictive control (CMPC). In order to improve the computing speed, a multiple objective model predictive control (MOMPC) is proposed. For the stability of the control system, the convergence of the DiMPC is discussed. Simulation tests are performed on the five different sub-loops of steam/water loop. The results indicate that the DiMPC may achieve similar performance as CMPC while outperforming the DeMPC method

Control of a train of high purity distillation columns for efficient production of 13C isotopes

It is well-known that high-purity distillation columns are difficult to control due to their ill-conditioned and strongly nonlinear behaviour. The fact that these processes are operated over a wide range of feed compositions and flow rates makes the control design even more challenging. This paper proposes the most suitable control strategies applicable to a series of cascaded distillation column processes. The conditions for control and input-output relations are discusssed in view of the global control strategy. The increase in complexity with increased number of series cascaded distillation column processes is tackled. Uncertainty in the model parameters is discussed with respect to the dynamics of the global train distillation process. The main outcome of this work is insight into the possible control methodologies for this particular class of distillation processes

Experimental investigation of reactor-loop transients during startup of a simulated SNAP-8 system

Primary loop transients during startup of Rankine cycle space power system in SNAP 8 simulato