Dynamic constrained optimization of chemical manufacturing

Abstract

System and method for chemical manufacture utilizing a dynamic optimizer for a chemical process including upstream and downstream processes. The dynamic optimizer includes a maximum feed calculator, operable to receive one or more local constraints on the downstream processes and one or more model offsets, and execute steady state models for the downstream processes in accordance with the local constraints and the offsets to determine maximum feed capacities of the downstream processes; and a feed coordinator, operable to receive the maximum feed capacities, and execute steady state models for the upstream processes in accordance with the maximum feed capacities and a specified objective function, subject to global constraints, to determine upstream production parameters for the upstream processes, which are usable to control the upstream processes to provide feeds to the downstream processes in accordance with the determined maximum feeds and the objective function subject to the global constraints.

Description

FIELD OF THE INVENTION [0001] The present invention generally relates to the field of chemical production. More particularly, the present invention relates to systems and methods for optimizing chemical production in a manufacturing process with downstream and / or upstream constraints using predictive control methodologies. DESCRIPTION OF THE RELATED ART [0002] Like any other commercial enterprise, those in the business of producing chemical products desire to maximize efficiencies and profitability, while meeting various constraints, such as, for example, raw material and energy costs, plant equipment limitations, product prices, and so forth. The ability to produce chemicals in such a manner may be further complicated for chemical plants producing more than one grade or type of chemical product. [0003] As shown in prior art FIG. 1, a chemical plant 104 may produce chemicals, including, for example, olefin, gasoline, and fuel oil, among others, of varying grades, from feedstock, e.g...

AI Technical Summary

Benefits of technology

[0059] Note that in preferred embodiments, the maximum feeds to each area of the cold section may be determined by executing the steady state models of the cold side ICOs in optimization mode with the feeds treated as manipulated variables (MVs). Appropriate prices may be set so that the optimizer will increase component feeds in proportion to their values until all degrees of freedom in the APC are used. The steady-state model for each cold side ICO may be matched to the plant by mapping the ICO steady-state biases to the appropriate biases in the steady-state model. MV and controlled variable (CV) limits and targets may also be mapped to the steady-state model. In performing the feed maximization calculation for a section, the maximum feed calculator may use the same model as the section's ICO, thus ensuring consistency. This approach may guarantee that each section's controller is capable of maintaining its CV targets at the feed rate and feed composition delivered by the feed coordinator.

[0060] The feed coordinator is thus preferably an ICO that may operate as a “master” controller for the furnace ICOs, setting their feed and severity targets. The primary goal is to achieve the maximum fe

Problems solved by technology

The ability to produce chemicals in such a manner may be further complicated for chemical plants producing more than one grade or type of chemical product.

However, the complexity of most real world systems generally precludes the possibility of arriving at such solutions analytically, i.e., in closed form.

Conventional computer fundamental models have significant limitations, such as:

(1) They may be difficult to create since the process may be described at the level of scientific understanding, which is usually very detailed;

(3) Some product properties may not be adequately described by the results of the computer fundamental models; and

(4) The number of skilled computer model builders is limited, and the cost associated with building such models is thus quite high.

These problems result in computer fundamental models being practical only in some cases where measurement is difficult or impossible to achieve.

Such models typically use known information about process to determine desired information that may not be easily or effectively measured.

This is very difficult to measure directly, and takes considerable time to perform.

However, there may be significant problems associated with computer statistical models, which include the following:

(1) Computer statistical models require a good design of the model relationships (i.e., the equations) or the predictions may be poor;

(2) Statistical methods used to adjust the constants typically may be difficult to use;

(3) Good adjustment of the constants may not always be achieved in such statist

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