Process control loops, AIMS

We will consider all the components of this temperature control loop in more detail later in this book. For now we need only appreciate the fact that the automatic control of some variable in a process requires the installation of a sensor, a transmitter, a controller, and a final control element (usually a control valve). Most of this book is aimed at learning how to decide what type of controller should be used and how it should be tuned, i.e., how should the adjustable tuning parameters in the controller be set so that we do a good job of controlling temperature. 

Naturally, the type of controller plays an important role. In this chapter we limit the analysis to classical PID controllers. These form over 90% of the control loops in industry. As mentioned, from a plantwide control viewpoint multi-SISO controllers are the most adapted. Naturally, we do not exclude more sophisticated MIMO control systems, as DMC or Model Based Control systems, but these are typically applied to stand-alone complex units, as FCC reactors, complex distillation units in refining, etc. Hence, the controllability analysis presented here aims more to get a conceptual insight in the dynamics of a process related to its design than to offer a high-performance control solution. 

Models can often be used to guide the designed experiments that need to be run on the commercial process as part of the qualification effort. Out of this activity come the aims and limits of the process variables, which either directly or indirectly are under closed-loop feedback control and some of which are then monitored by statistical process control techniques (Ref 2). 

The discussion above naturally leads into the design of the control strategies for the entire new plant. Part of the job of the regulatory control structure is to minimize the variability of the key process variables in the face of inevitable process disturbances. The regulatory control loops must keep the process at the specified operating conditions (stability) and must allow on-aim control of the key process variables (performance) within the allowable operating window. 

ABSTRACT In most cases, Model Based Safety Analysis (MBSA) of critical systems focuses only on the process and not on the control system of this process. For instance, to assess the dependability attributes of power plants, only a model (Fault Tree, Markov chain. ..) of the physical components of the plant (pumps, steam generator, turbine, alternator. ..) is used. In this paper, we claim that for repairable and/or phased-mission systems, not only the process but the whole closed-loop system Proc-ess/Control must be considered to perform a relevant MBSA. Indeed, a part of the control functions aims to handle the dynamical mechanisms that change the mission phase as well as manage repairs and redundancies in the process. Therefore, the achievement of these mechanisms depends on the functional/dysfunctional status of the control components, on which these functions are implemented. A qualitative or quantitative analysis method which considers both the process and the control provides consequently more realistic results by integrating the failures of the control components that may lead to the non-achievement of these mechanisms. This claim is exemplified on an industrial study case issued from a power plant. The system is modeled by a BDMP (Boolean logic Driven Markov Process), assuming first that the control components are faultless, i.e. only the faults in the process are considered, and afterwards that they may fail. The minimal cut sequences of the system are computed in both cases. The comparison of these two sets of minimal cut sequences shows the benefit of the second approach. 

The following quality control loop is set up to use the information about the customer satisfaction (Figure 5). Input for the process are the 5 M man, machine, material, method and management. The processes are the controlled system. The output of the processes are services, e.g. research results. The service quality can be determined by questionnaires or interviews. The instrument customer survey can help to detect where the institutes and chairs failed to meet the customer requirements and to satisfy their customers. The institutes and chairs must define areas for improvement as well as suitable measures for improvements. The aim is to prevent deviation and customer dissatisfaction in the future. 

The dynamic behaviour of batch process units changes with time and this makes their precision control difficult. The aim of this paper is to highlight that the slave process of batch process units can have a more complex dynamics than the master loop has, and very often this could be the reason for the non-satisfying control performance. Since the slave process is determined by the mechanical construction of the unit, the above mentioned problem can be effectively handled by a model-based controller designed using an appropriate nonlinear tendency model. The paper presents the structure of the tendency model of typical slave processes and presents a case study where real-time control results show that the proposed methodology gives superior control performance over the widely applied cascade PID control scheme. 

We stress that design for controllability can either aim at reducing control bandwidth limitations, imposed by fundamental process properties, or at reducing the control requirements imposed by disturbance sensitivities. Based on results from linear systems theory we have presented simple model based tools, based on the decomposed models above, which can be used to improve stability, non-minimum phase behavior and disturbance sensitivities in plants with recycle. One important conclusion of the presented results is that the phase-lag properties of the individual process units play a crucial role for the disturbance sensitivity of an integrated plant. In particular, by a careful design of the recycle loop phase lag, it is possible to tailor the effect of process interactions such that they serve to effectively dampen the effect of disturbances in the most critical frequency region, that is, around the bandwidth of the control system. 

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