Plant Dynamics with Control System

In this section, the plant stability of the Super LWR is checked by analyzing the response to the following perturbations with the control system designed and tuned in Sect. 4.4. 

The same operational goal as used in BWRs is applied here. Thermal-hydraulic stability and coupled neutronic thermal-hydraulic stability of the Super LWR are described in Chap. 5. 

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The plant-wide control system developed before is finally applied to the modelled process in HYSYS.PLANT and evaluated based on its close-loop dynamic behaviour and disturbance rejections performance. Once the control system of the non-heat integrated plant is validated, the favoured HEN design, based on its operational performance, is integrated within the entire plant and its control system is linked with the proposed plant-wide control structure mainly through cascade control configurations. 

A consideration of dynamics should be factored into the design of a plant at an early stage, preferably during pilot-plant design and operation. It is often easy and inexpensive in the early stages of a project to design a piece of process equipment so that it is easy to control. If the plant is designed with little or no consideration of dynamics, it may take an elaborate control system to try to make the most of a poor situation. 

With an appropriate specification of the system performance weighting and objective function, a generalized plant dynamics is established, as shown in Fig. 22.2. The feedback controller processes the measured signal y to determine the injection rate of the control fuel rhin based on a regulated relationship between variables w and z, where w is associated with disturbance and uncertainty, and 2 with the objectives of system performance and stability. 

A comprehensive framework of robust feedback control of combustion instabilities in propulsion systems has been established. The model appears to be the most complete of its kind to date, and accommodates various unique phenomena commonly observed in practical combustion devices. Several important aspects of distributed control process (including time delay, plant disturbance, sensor noise, model uncertainty, and performance specification) are treated systematically, with emphasis placed on the optimization of control robustness and system performance. In addition, a robust observer is established to estimate the instantaneous plant dynamics and consequently to determine control gains. Implementation of the controller in a generic dump combustor has been successfully demonstrated. 

The plant control system functions to limit temperature rates of change during plant load change and upset events. This is achieved at two different levels. In the time asymptote the control system through control variable set points (with values assigned as a function of steady-state power) takes the plant to a new steady-state condition. The set point values are chosen so that hot side temperatures remain little changed. In the shorter term the control system manages the dynamic response of the plant so that the transition between steady states is stable and with minimal overshoot of process variables. 

Throughout the design of a chemical plant, issues relating to safety, economics and environmental impact must be considered. By doing so, the risks associated with the plant can be minimised before actual construction. The same principle applies whatever the scale of the process. The field of process control (Chapter 8) considers all these issues and is, indeed, informed by the type of hazard analyses described in Chapter 10. The objectives of an effective control system are the safe and economic operation of a process plant within the constraints of environmental regulations, stakeholder requirements and what is physically possible. Processes require control in the first place because they are dynamic systems, so the concepts covered in the earlier chapters of this book are central to process control (i.e. control models are based on mass, energy and momentum balances derived with respect to time). Chapter 8 focuses on the key aspects of control systems. 

As seen in the previous chapter, all the approaches used to solve the dynamie optimization problem integrate, at some point, the dynamical system of the ehemieal proeess. In order to obtain more effieiently the values of the optimum profile of the control variable, a suitable model of the system should be developed. That means that the complexity of the model should be limited, but, in the same time, the model should represent the plant behaviour with good accuracy. The best way to obtain such a model is by using the model reduction techniques. However, the use of a classical model reduction approach is not always able to lead to a solution [6]. And very often, the physical structure of the problem is destroyed. Thus, the procedure has to be performed taking into account the process knowledge (units, components, species etc.). 

Dynamic simulation may be used for off-line or on-line applications. An offline dynamic model runs independent of the plant. This is a predictive mode in which a column or an entire process is simulated to predict transient behavior with no input from the plant. Such a model is typically used for the design of equipment and control strategies and as a training simulator. An on-line dynamic model may be used to monitor the column performance and to provide vital information for the control system. It reads current plant conditions and, in real time, computes properties that are not measured on-line, such as product compositions. This makes it possible to control such properties directly. The on-line dynamic simulator can also predict future column trends, thereby allowing the control system to take corrective action in advance. 

The simulation work which was done was also the basis for the development of an operator training simulator. This allowed the operators to gain experience before working with the real plant, to its dynamic responses and also to familiarise them with the distributed control system (DCS). 

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