Input/Output controllability analysis

The alternative designs generated for the two test cases (heat exchanger and binary distillation) on basis of this TCA principle prove to be remarkably well aligned with the results of a black-box, input-output controllability analysis using the steady-state disturbance sensitivity approach. 

Appropriate guidance documents should provide clear checklists and assessment criteria that can be used by both modelers and regulators. Model documentation should include why a specific model type and complexity has been selected documentation should also include information about model inputs, outputs, assumptions, analysis, interpretation, limitations, and uncertainties. It should be demonstrated that a model is sensitive enough to show adverse effects (positive control). It was also suggested during the workshop that standardized submodels could facilitate the development and use of EMs for regulatory decision making. 

The methodology presented hereafter regards a MIMO system that can be handled by a combination of multi SISO loops. It is an input/output controllability being based on linear analysis tools. It can be applied to a stand-alone complex unit, as a distillation column, or to a flowsheet. In this later case it has the character of a decentralised (integral) plantwide control problem. 

The results provided by the tools for input-output-controllabiUty analysis presented here therefore give important hints for possible changes of the plant design, beyond the selection of control structures and controller structures. 

Data about the plans and routines used by workers in controlling a process can be obtained by means of an "activity analysis," a type of input-output analysis. A chart can be made showing how certain process indicators change over time in response to changes of the control settings. From this chart it is possible to determine the type of process mformation that workers use to carry... 

Failure to meet the requirements of the validation protocol with respect to process inputs and output control should be subjected to requalification following a thorough analysis of process data and formal review by the CMC Coordination Committee. 

Control based on neural network. Similar to fuzzy logic modeling, neural network analysis uses a series of previous data to execute simulations of the process, with a high degree of success, without however using formal mathematical models (Chen and Rollins, 2000). To this goal, it is necessary to define inputs, outputs, and how many layers of neurons will be used, which depends on the number of variables and the available data. 

We give only a short description of the three supply chain configurations and their simulation models for details we refer to Persson and Olhager (2002). At the start of our sequential bifurcation, we have three simulation models programmed in the Taylor II simulation software for discrete event simulations see Incontrol (2003). We conduct our sequential bifurcation via Microsoft Excel, using the batch run mode in Taylor II. We store input-output data in Excel worksheets. This set-up facilitates the analysis of the simulation input-output data, but it constrains the setup of the experiment. For instance, we cannot control the pseudorandom numbers in the batch mode of Taylor II. Hence, we cannot apply common pseudorandom numbers nor can we guarantee absence of overlap in the pseudorandom numbers we conjecture that the probability of overlap is negligible in practice. 

Sinusoidal Forcing. From the standpoint of control analysis the most useful forcing function is the steady state since wave. If a steady-state, low amplitude sinusoidal variation is imposed on some property of an inlet process stream, the same property of the corresponding outlet stream will also vary sinusoidally and at the same frequency. For most process components the output wave will lag behind the input wave, and the output amplitude will be less. 

Analysis of Control Input-Output Interactions in Dynamic Systems, by L. S. Tung andT. E Edgar, AIChEJ., 27(4), 690 (1982). 

Part III (Chapters 6 through 12) is devoted to the analysis of static and dynamic behavior of processing systems. The emphasis here is on identifying those process characteristics which shape the dynamic response for a variety of processing units. The results of such analysis are used later to design effective controllers. Input-output models have been employed through the use of Laplace transforms. 

The use of Laplace transforms allows us to form a very simple, convenient, and meaningful representation of chemical process dynamics. It is simple because it uses only algebraic equations (not differential equations, as we have seen in Part II). It is convenient because it allows a quick analysis of process dynamics and finally, it is meaningful because it provides directly the relationship between the inputs (disturbances, manipulated variables) and the outputs (controlled variables) of a process. 

The Laplace transforms allowed us to develop simple input-output relationships for a process and provided the framework for easy analysis and design of loops with continuous analog controllers. For discrete-time systems we need to introduce new analytical tools. These will be provided by the z-transforms. 

The development of input-output models for discrete-time systems, which constitute the basis for the dynamic analysis and design of control loops... 

In the preceding section the analysis was centered around the response of the discrete components in a direct digital control (DDC) loop with characteristic representative the control algorithm. The use of z-transforms allowed easy and straightforward development of simple input-output models through the discrete transfer functions. 

During the transient from the old to the new set point, we record the values of the manipulated variable and the controlled output. These values are shown in Table 31.2. Linear regression analysis using the input-output data of Table 31.2 produces the following values for the process parameters. 

Consequently, as the set point value changes we compute new values for xp and Kp using linear regression analysis on the input-output data. Then from eq. (16.1) we can compute the new value of the controller gain. This procedure can be repeated on-line every time we change the set point value. 

Noise is the result of random error due to control input/output functions, errors in analysis, digital dither in the electronics, and a potential host of presumably random causes. The noise level may be constant, or may vary over the range of data gathered. In either... 

Hierarchical Approach is a simple but powerful methodology for the synthesis of process flowsheets. It consists of a top-down analysis organised as a clearly defined sequence of tasks grouped in levels. Each level solves a fundamental problem as, number of plants, input/output structure, reactor design and recycle structure, separation system, energy integration, environmental analysis, safety and hazard analysis, and plantwide control. At each level, systematic methods can be applied for the synthesis of subsystems, as chemical reaction, separations, or heat exchangers network. 

The controllability tools presented in here are based on the theory of linear systems, which is valid for relatively small disturbances around the stationary state. A non-linear approach, more suited for investigating the effect of large variations, will be developed in Chapter 13. The chapter starts with a brief introduction in process dynamics, followed by the properties of linear systems. The controllability analysis begins with SISO (single input/single output) systems and reviews the major concepts in feedback control. Then, the analysis is extended to MIMO (multi input/multi output) systems, with emphasis on decentralised control systems (multi SISO control loops), which is the most encountered in plantwide applications. 

Consistent results in controllability analysis demand the use of scaled variables (u,d,y,r ). Scaling consists of evaluating the magnitude of expected disturbances and reference changes, of available manipulated inputs, and of the allowed deviation of controlled outputs. Scaling is achieved by dividing each variable by its maximum expected or allowed change ... 

In a multi-input multi-output (MIMO) control system (Fig. 12.14), there are several controlled variables (vector y) that should be kept on set-points (vector r) faced to disturbances (vector d) by means of appropriate manipulated variables (vector u). The feedback controller K provides the algorithm that will ensure the link between the manipulated (inputs) and controlled (outputs) variables. In this chapter we will consider a decentralised control system that makes use of multi-SISO control loops, which means that a single controlled variables is controlled by a single manipulated variable. This arrangement is typical for plantwide control purposes. However, there will be interactions between different loops. These Interactions can be detrimental, or can bring advantages. Therefore, the assessment of interactions is a central issue in the analysis of MIMO systems. 

Some alternatives can be rejected during the steps 4-6 when controllability analysis indicates clearly inferior dynamic behaviour. However, some design improvements can be suggested by the controllability measures, as described at the points 4 and 6. Essentially, they should ensure 1) The effect of interactions should not prevent the implementation of a decentralised control system (RGA and RGA number), 2) The magnitude of inputs must be effective in controlling the outputs at steady-state (SVD and CLDG analysis). 

The Battelle-NBS study (1) used an economic input/output analysis to estimate the cost of corrosion in the United States. In the model, the US economy was divided into 130 industrial sectors. For each sector, estimates were made on the costs of corrosion prevention, as well as the cost of repair and replacement because of corrosion. The following direct costs were included in the study replacement of equipment or buildings loss of product maintenance and repair excess capacity redundant equipment corrosion control such as inhibitors organic and metallic coatings engineering research and development testing design insurance parts and equipment inventory. 

---