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Top-down analysis (focus on steady-state economics) begins with Step 1—Define operational objectives (economic cost J to be minimized) and constraints. A systematic approach to plant-wide control requires that we first quantify the operational objectives with a scalar cost function J (or equivalently, a scalar profit function, P = -J). This is usually not very difficult, and typically we have J = cost feed + cost utilities (energy) – value products.
The goal of operation (and of control) is to minimize the cost J (or equivalently, to maximize the profit P = - J). The cost J should be minimized subject to satisfying the operational constraints, including safety and environmental constraints.
Typical operational constraints are minimum and maximum values on flows, pressures, temperatures and compositions. For example, we always have that all flows, pressures and compositions must be non-negative.
Step 2—Identify degrees of freedom (MVs) and optimize the operation for important disturbances (offline analysis) to identify regions of active constraints. Identifying the economic (usually steady-state) degrees of freedom is not usually as simple as one would expect. One approach is to first identify all the (dynamic and steady-state) control degrees of freedom (inputs, "valves"), and then subtract the ones with no steady-state effect. The optimization of the plant for expected operation is usually the most time- consuming step. Note that there are two main modes of operation Mode I. Given throughput. Maximize efficiency.
Mode II. Throughput is a degree of freedom. Find optimal throughput (often this equals maximum throughput). Maximize throughput (with production rate as a degree of freedom).
Although the plant and its control system are most often designed for Mode 1, usually the most profit is made when product processes are high and optimal operation is the same as maximum throughput (Mode II). Plant-wide control should generally be focused much more on Mode II.
Step 3—Selection of primary controlled variables (CV1) (Decision 1). "What should we control?" This question has been the main topic for my research in the plant-wide control area over the last 25 years, and I think I finally have found the solution:
In particular, the research has been focused towards the latter unconstrained CV1s. "Self-optimizing" means that when the selected variables are kept constant at their setpoints, then the operation remains close to its economic optimal in spite of the presence of disturbances.
In general, the controlled variables (CV1) will be individual measurements or combinations of measurements. Thus we can write, CV1 = H y. Here, y contains all the available measurements (including MVs) and H is the selection or combination matrix to be found. [See V. Alstad, S. Skogestad and E.S. Hori, "Optimal Measurement Combinations as Controlled Variables," J. Proc. Control, 19, 138-148, 2009.]
Step 4—Select location of throughput manipulator (TPM) (Decision 3).
The main purpose of a process plant is to transform feedstocks into more valuable products, and this involves moving mass through the plant. The amount of mass moved through the plant, as expressed by the feed rate or product rate, is determined by specifying one degree of freedom, which we call the TPM.
In addition, most plants have one TPM, but complex plants with six parallel units and multiple or alternative products may have more. From a steady-state point of view, the location of the TPM does not matter, but it is important dynamically.
There are two main concerns when placing the TPM:
An underlying assumption for the radiation rule is that we want "local consistency" of the inventory control system. [See E.M.B. Aske and S. Skogestad, "Consistent Inventory
Control," Ind. Eng. Chem. Res, 48 (44), 10892-10902, 2009.] This means that the inventory in each unit is controlled
locally, that is, by its own inflows or outflows.
In theory, one may not require local consistency, and allow for "long" inventory loops, but this is almost never done for obvious operational reasons, including risk of emptying or overfilling tanks, start-up and tuning and increased complexity.
Bottom-up analysis (focus on dynamics) begins with Step 5—Choose structure of regulatory (stabilizing) layer. The purpose of the regulatory layer is to stabilize the plant, preferably using a simple control structure with single-loop PID controllers. "Stabilize" here means the process doesm’t drift away from acceptable operating conditions when there are disturbances. Also, the regulatory layer should follow the setpoints given by the "supervisory layer."
Reassignments (logic) in the regulatory layer should be avoided. Preferably, the regulatory layer should be independent of the economic control objectives (regions of steady-state active constraints), which may change, depending on disturbances, prices and marked conditions. The main decisions are:
Step 6—Select structure of supervisory control layer. Objectives of supervisory layer:
Step 7—Select structure of (or need for) optimization layer (RTO). Ask the question, is this even necessary? Do we need such a structure?
Alternative 1—"Advanced single loop control" = PID control with possible "fixes," such as feed-forward (ratio), decouplers, logic, selectors and split-range control. (In many cases some of these tasks are moved down to the regulatory layer). With single-loop control an important decision is to select pairings. Note that the issue of finding the right pairings is more difficult for the supervisory layer because the interactions are usually much stronger at slower time scales.
Alternative 2—Multivariable control (usually MPC).
This article presents the current version of the systematic plant-wide control procedure. It’s still being updated and tested on applications, but after having worked on this issue of about 25 years, I have good hopes of converging at final procedure by about the year 2025.
[Editor’s note: An extended version of this article is at www.controlglobal.com/whitepapers/2011/004.html.]