2 PROCESS DESIGN AND PLANT-WIDE CONTROL
2.6 CONTROLLER CONFIGURATION AND CONTROL LAW
Once the controlled variables that best translate the economic objectives to control objectives have been identified, the synthesis of information flow from measured to manipulated variables follows. This may be a non-trivial task should significant overlapping interdependencies exist between measured and manipulated variables. It is important to understand that the methodology in section 2.4 only selects a possible optimal set of controlled variables, but does not suggest how these controlled variables should be paired with the available manipulated variables. Incomplete information compounds the synthesis process requiring robust solutions to uncertainties. A sensitivity analysis is paramount to ensure robust performance (Stephanopoulos, 1983). Morari et al. (1980) stated that it is desirable to define a control structure with the lowest degree of complexity necessary to accomplish the control task. Given the unavoidable mismatch between the actual process and the process model, the "lowest control structure complexity" should rather be substituted with the most pliable structure (i.e., affording plasticity) to account for uncertainty. Plant-wide control structures have ranged from decentralised PID to MPC strategies. Should the performance of a decentralised PID strategy compare to a MPC
implementation, industry will necessarily adopt the decentralised PID strategy. Structural simplicity and operator acceptance favour a decentralised PID strategy. Conversely, should the MPC control offer substantial performance improvements, economic considerations outweigh historic preference (Robinson et al., 2001). PID strategies serve as a reduced model to a plant-wide control problem, owing to the perceived simplicity of design and reduced engineering time.
Decentralised control is motivated by a decomposed control systems that may be less prone to model uncertainty. Process judgement imposes a certain control configuration, which explicitly provides process information. A centralised controller (e.g., MPC) would need to obtain such process information from a dynamic process model. However, process judgement is frequently subjective and non-intuitive process behaviour may result in incorrect control configurations, which impact the rest of the control design. Furthermore, efficient decoupling of decentralised SISO controllers requires a dynamic model, otherwise decoupling is also based on trial-and-error tuning. Decentralised control is naturally preferred for non-interacting processes, but few chemical plants are non-interacting. Multivariable control (e.g., MPC) improves the control performance of interacting processes dramatically and provide robust tracking of moving constraints (Larsson & Skogestad, 2000).
Despite the clear benefit of a non-linear, multivariate approach to plant-wide control, research on plant-wide control has remained confined to linear multi-loop SISO control structures (e.g. simple PID control, cascade control and ratio control). Nevertheless, considerable advances in the industrial implementation of large model predictive and other multivariable control systems have been made. Simple multivariable model predictive control structures overcome the pairing and tuning complexities arising from multi-loop SISO control implementations. Control methodologies such as model predictive control, robust control and real-time process optimisation reduce the dependency on multi-loop SISO controllers.
Once the controlled variables and manipulated variables have been selected, the regulatory and supervisory control layers are designed. The regulatory layer has a stabilising and disturbance rejection function. The regulatory design addresses controllability such as the pairing of controlled and manipulated variables. Bristol's relative gain array may have limited use for highly non-linear processes. Most control configurations are comprised of nested control loops, which make use of secondary measurements. Secondary measurements are used in fast inner control loops, where local disturbances are rejected before these disturbances impact on the slower outer control loop. Secondary measurements should be selected so that updates to their set points by the outer loops are minimal. (Larsson & Skogestad, 2000).
Table 2-8 - Methodology proposed by Price et al. (1994)
Hierarchical decomposition on control objectives (Price et al. (1994)
1. Inventory and production rate control 2. Product purity control
3. Operating and equipment constraints 4. Improved economic performance
Selecting the controlled variables in section 2.4 involved a top-down approach, whereas the control system design is a bottom-up approach. Plant-wide control departs from the unit operation based approach, which has proven unsuccessful for plants with significant material recycle and heat integration. Plant-wide control follows a hierarchical decomposition based on either process structure or throughput, control objectives or time scales. Luyben et al. (1997) proposed the approach in Table 2-5.
Price et al. (1994) proposed the methodology as outlined in Table 2-8, which first stabilises the process and determines the throughput manipulator, after which the production specifications are addressed. McAvoy and Ye (1994) based their decomposition on time scales. McAvoy and Ye (1994) proposed a four stage method as described in Table 2-9. Steps 1-3 are based on control loop speeds. Step 1 rejects local disturbances, while step 2 involves screening with controllability tools such as RGA.
The selection of the sets of controlled and manipulated variables is unavoidably related to the performance of the final control system. The selection of the input- output connections, that form the control loops, must be considered simultaneously with the quantitative evaluation of the developed control system. Zheng et al. (1999) evaluated a large number of different control structures, control laws and tuning parameters based on the integral absolute error of the product purity during process disturbances. Owing to this combinatorial problem, Zheng et al. (1999) highlighted the difficulty in selecting a set of controlled variables and emphasised the lack of an efficient method.
Theoretical considerations in pairing controlled and manipulated variables rest on controllability measures such as the relative gain array (RGA), singular values and the condition number along with singular vectors. The benefit of these controllability techniques has been evident for unit operations, but less so for effective structuring of plant-wide control systems (Stephanopoulos & Ng, 2000). Stephanopoulos & Ng (2000) proposed using the modular multivariable controller design that selects the best set of controlled variables based on (1) each manipulated variable's effect on the controlled variable (i.e., analysis of the open-loop gain), (2) the model uncertainty and (3) the non-minimum phase behaviour of the input-output relationships.
Table 2-9 - Hierarchical decomposition based on time scales.
Hierarchical decomposition based on time scales (McAvoy & Ye, 1994)
1. Design inner cascade loops
2. Design basic decentralised loops, other than the control loops associated with purity and production rate.
3. Production and purity control loops. 4. Higher layer controls.
Once a control structure has been designed, real-time optimisation, using steady state models and operational objectives, should update the set points for the controlled variables (Larsson and Skogestad, 2000). Particularly for large disturbances and shifting market conditions, the new set points may be located far from the original operating region. The PID tuning parameters in this new operating region may also need revision for highly non-linear processes. The final process control system must be validated as an important final step through non-linear dynamic simulation (Larsson and Skogestad, 2000).
2.7 CONCLUDING REMARKS
As discussed in the following two chapters, evolutionary reinforcement learning (ERL) circumvents the use of heuristic methods in plant-wide control designs. Though not algorithmic, or strictly mathematical in nature, evolutionary reinforcement learning offers real opportunities. ERL does not require identification of major disturbances or a throughput manipulator, which may be non-trivial tasks. ERL implicitly identifies the necessary controlled variables and implicitly pairs the controlled variables with the available manipulated variables in a non-linear multivariate controller.
Self-optimisation, as defined by Skogestad (2000a), appears plagued by heuristic considerations that may not produce the desired result. ERL methods, such as symbiotic memetic neuro-evolution (SMNE) also seek self-optimising control but aim to achieve this via neural network generalisation, which requires no explicit disturbance identification. The efficient generalisation of neural networks to novel process conditions, typically allows robust performance despite significant disturbances. Where robust generalisation is not achievable, adaptive control via adaptive neural swarming (ANS) provides for on-line adaptation of neural network weights to track a changing economic optimum.
Chapter 3 introduces introductory concepts for efficient reinforcement learning. Chapter 4 presents the neurocontrol algorithms SMNE and ANS that form the cornerstone of a plant-wide neurocontrol strategy.