Plant control optimizing

S. Moorthy and P. Iyer, "Neural Network Simulation for Plant Control Optimization," in Proceedings of the Industrial Computing Conference, ISA, Anaheim, Calif., 1991. 

To control, optimize, or evaluate the behavior of a chemical plant, it is important to know its current status. This is determined by the values of the process variables contained in the model chosen to represent the operation of the plant. This model is constituted, in general, by the equations of conservation of mass and energy. 

In supervisory control, process and economic models of the plant are used to optimize the plant operation by maximizing daily profit, yields, or production rates. The computer program reviews operating conditions periodically, computes the new conditions that optimize a chosen objective function, and adjusts plant controller set points, thus implementing the new improved conditions. This scheme will obviously require a model of the plant, current information about operating conditions from the plant s control system, and finally, sophisticated optimization software. 

B. Stancato and co-workers, "Advanced Control/Optimization for Mitsubishi Kashima Ethylene Plant," paper presented at AIChE Spring Meeting, Houston, April 7—11,1991. 

Houk, B. G. Kelly, D. N. Davenport, S. L. and Fatora HI, F. C., "Closed-Loop Plant-Wide Optimization and Advanced Control Technologies Capture Many Benefits", Paper Presented at the International Conference on Productivity and Quality in the Hydrocarbon Processing Industry (1992). 

In unit operations control, the individual column variables are treated only as constraints and so long as the values of these constraints are within acceptable limits, the column is controlled (optimized) to maximize production rate, profitability, etc. Economics of individual fractionators may continually change throughout the life of the plant, because energy savings can be important at one particular time, whereas product recovery can be more important at other times. 

Real Time Optimization. This module receives the values of the variables of the plant, performs reconciliation on these values. This node has a steady state (mathematical, physically based) model of the plant. An optimization is made using that model every hour or so. The optimization results are sent to the lower level, the supervisory control. These results are the new set points of the controlled variables. The best operating point of the plant (which means a set of set points values) is calculated in each optimization. The optimization takes into account constraints on the variables (limited change in manipulated variables, safety, quality, etc. constraints in controlled variables). The node uses as well a historian module with past data of the plant. 

G. M. Belland, R. G. Pressacco and W. Van Beek, "Leaching Plant Flow and Level Control Optimization at Cominco s Trail Operations", Zinc and Lead 95. T. Azakami, Ni Masako, J.E. Dutrizac and E. Ozberk, Eds., Mining and Mineral Processing Institute of Japan, Tokyo, Japan, 1995, 599-611. 

A distributed control system may involve the use of microcomputers at the local level and the use of more powerful rruchines to coordinate overall plant control objectives. In this context, it has been suggested that process control might be described better as process management." " Local control of the operation of individual separators is still important but, with the use of distributed control, reliability is maintained (microcomputers are dedicated to particular process units) while overall technical and economic objectives are pursued (mainframe computers can perform complex on-line/off-line optimization). The advantage to distributed control is that it makes effe ve use of current technology and provides a framework within which control and optimization developments can be implemented. These developments probably will include better simulation and optimization routines that will help to assess the current state of the process plant and to suggest improvements. 

On the last day of this trial, plant conditions optimized and SRU throughput of Acid Gas gradually increased from 17,000 Sm3/h to around 18,000 Sm3/h as bellow leak remained under control and flaring of acid gas completely stopped. 

Control and managing of industrial plants with the right 1 C components and subsystems for optimal operational efficiency and safety is not an easy task. It is a quite complex job, especially when system availability and avoidance of spurious tripping is important simultaneously with plant safety. Conventionally, there are two parts in the plant control and safety. These are BPCS and SIS as already discussed in previous chapters. Basically each of these systems consists of ... 

Ready for plant design, optimization, and model-based control. 

Many of the gas turbines applied in electric utility combined-cycle service are supphed as part of a complete package by the gas turbine manufacturer. The manufacturer supphes or specifies aU the major equipment, such as heat-recovery steam generators (HRSGs), steam turbines, and plant controls, to optimize plant performance through an integrated approach. Table 6.24 lists typical performance specifications for three versions of combined cycles based on the MS7001E gas turbine. 

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