Jump to content

Process optimization

From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by MrGSA (talk | contribs) at 22:34, 13 January 2009. The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Process optimization is the discipline of adjusting a process so as to optimize some specified set of parameters without violating some constraint. The most common goals are minimizing cost, maximizing throughput, and/or efficiency. This is one of the major quantitative tools in industrial decision making.

In chemical engineering terms, a process is typically a set of equipment arranged, controlled, and operated in a particular way, to produce a product. The product must meet certain specifications, such as a certain production rate, product quality, and cost. Typically, specifications are supplied as a range of values, such as "Must be between 84% and 87% octane", or "must cost less than $250 per ton."

When optimizing a process, the goal is to maximize one or more of the process specifications, while keeping all others within their constraints.

Areas

Fundamentally, there are three parameters that can be adjusted to affect optimal performance. These are:

The first step is to verify that the existing equipment is being used to its fullest advantage by examining operating data to identify equipment bottlenecks.

  • Operating procedures

Operating procedures may vary widely from person-to-person or from shift-to-shift. Automation of the plant can help significantly. But automation will be of no help if the operators take control and run the plant in manual.

  • Control optimization

In a typical processing plant, such as a chemical plant or oil refinery, there are hundreds or even thousands of control loops. Each control loop is responsible for controlling one part of the process, such as maintaining a temperature, level, or flow.

If the control loop is not properly designed and tuned, the process runs below its optimum. The process will be more expensive to operate, and equipment will wear out prematurely. For each control loop to run optimally, identification of sensor, valve, and tuning problems is important. It has been well documented that over 35% of control loops typically have problems.[citation needed]

The process of continuously monitoring and optimizing the entire plant is sometimes called performance supervision.

The global optimization problem is a subject of intense current interest. Finding the optimal solution to a complex optimization problem is of great importance in a variety of fields, such as protein structure prediction, microprocessor circuitry design, and economics science. Simulated annealing (SA) methods have been applied successfully to the description of several global extremization problems. Moreover, they have attracted great interest because of their suitability for large-scale optimization problems, especially for those in which a desired global minimum occurs among other local minima. In the domain of the atomic and molecular aggregates, for example, the discovery of the lowest-energy conformations for biological macromolecules or crystal structures for systems with known composition is a frequent goal. In particular, the generalized simulated annealing (GSA) approach has been used with success in predicting new three-dimensional protein structures and protein folding, fitting the potential energy surface for path reaction and chemical reaction dynamics, gravimetry problems, and mechanical properties in alloys, among others.

See also

Retrieved from "https://en.wikipedia.org/w/index.php?title=Process_optimization&oldid=263908166"