Читать книгу Profit Maximization Techniques for Operating Chemical Plants - Sandip K. Lahiri - Страница 70

PID control formed the backbone of a control system and is found in a large majority of CPIs. PID control has acted very efficiently as a base layer control over many decades. However, with the global increase in competition, process industries have been forced to reduce the production cost and need to maximize their profit by continuous operation in the most efficient and economical manner.

Most modern chemical processes are multivariable (i.e. multiple inputs influence the same output) and exhibit strong interaction among the variables (Lahiri, 2017b).

In a process plant, it is only seldom that one encounters a situation where there is a one‐to‐one correspondence between manipulated and controlled variables. Given the relations between various interacting variables, constraints, and economic objectives, a multi‐variable controller is able to choose from several comfortable combinations of variables to manipulate and drive a process to its optimum limit and at the same time achieve the stated economic objectives. By balancing the actions of several actuators that each affect several process variables, a multi‐variable controller tries to maximize the performance of the process at the lowest possible cost. In a distillation column, for example, there can be several tightly coupled temperatures, pressures, and flow rates that must all be coordinated to maximize the quality of the distilled product.

Advance process control (APC) is a method of predicting the behavior of a process based on its past behavior and on dynamic models of the process. Based on the predicted behavior, an optimal sequence of actions is calculated. The first step in this sequence is applied to the process. Every execution period a new scenario is predicted and corresponding actions calculated, based on updated information.

The real task of APC is to ensure that the operational and economic objectives of the plant are adhered to at all times. This is possible because the computer is infinitely patient, continuously observing the plant and prepared to make many, tiny steps to meet the goals (Lahiri, 2017b).

APC has established itself as a very efficient tool to optimize the process dynamically, minimize variations of key parameters, and push the plant to multiple constraints simultaneously and improve the profit margin on a real‐time basis.