Continuous processes require a control system that can minimize cost and optimize grade changes. Multiple products and grades are often made from the same feedstock in a continuous plant. However, since a plant cannot be shutdown between grades, a change-over period occurs during which the product is not on-spec. Thus, shortening the change-over period can minimize the amount of off-spec material being made during grade changes.
Integrated DCSs are essential to control complex manufacturing situations. A single control loop can be handled with traditional instruments alone. However, controlling complex situations requires a broader span of information than is available through discrete instrumentation.
Continuous processes rely heavily on regulatory controls and inferred variables. The DCS must also interface with highly sophisticated instruments and analyzers that have some control capabilities of their own and offer significant inference capabilities. For example, the Btu value of fuel cannot be measured unless it is burned in a calorimeter, but then it is not a useful process control variable.
However, the heating value of the fuel can be inferred from the thermal conductivity, temperature, and pressure of the incoming nature gas.
DCSs achieve high degrees of process accuracy because they incorporate process data archives which allow a better understanding of the process. These data archives allow people other than the operator to view and understand what happens in the process and be able to work on improving it. Optimum operation no longer depends on the operator monitoring the process every minute. Instead, the control system watches the process every second.
Many projects can be identified as being beneficial to the business. These projects frequently require the timely, accurate, and comprehensive information provided by DCSs as input. Such projects encompass a variety of areas including process optimizers, expert systems, quality lab interfaces, statistical process control, and plantwide maintenance programs.
Process optimization is a natural progression of the DCS. A company can use the information available through the DCS archives to optimize process conditions to conserve energy or save raw materials. They can also use the information to modify product mixes and product specifications to optimize costs on a global, rather than individual unit, basis (Funk and McAllister 1989)
Process optimizers provide advanced supervisory control. They monitor the current operating conditions, run advanced algorithms, and return recommended set-point changes to the control systems. However, process opti mizers control on such a large scale that separate computer hardware is needed for processing.
Expert systems are another progression of the DCS. They draw process database information from the DCS. Often, these systems are small to midsize and serve one of the two functions. One function is diagnostic, such as determining the cause of an equipment malfunction or maintenance troubleshooting. This analysis begins with embedding an operator's experience in the system's rule database and using it to work through all complicated variations and combinations of conditions related to faults or alarms (Funk and McAllister 1989).
The other function of an expert system is to work through situations that have a high degree of uncertainty and develop a knowledge base from the results of decisions. For example, knowledge about the best way to cut costs given the nature of raw materials and process conditions can be put into a rule database. Then, the system monitors the results using that knowledge and improves the knowledge base (Funk and McAllister 1989).
Other applications include processes where process dynamics change over time, such as models or controllers that require frequent updating. A fixed-bed reactor which shows how the catalyst's aging affects process dynamics is another example.
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