CARACTERÍSTICAS CONSTRUCTIVAS PARTICULARES DE UN ESTABLECIMIENTO DE ALOJAMIENTO

Morari et al. (1980) stated that the principal objective of a control system is to translate economic objectives into process control objectives. Likewise, the objective of a chemical plant is the "maximisation of the generated business-wide economic value via plant operation" (Stephanopoulos and Ng, 2000). This generated value is therefore reduced by process upsets owing to sustained operation at sub-optimal conditions or off-spec production, down-time owing to failures, long transition periods from one operating mode to another. The mismatch between the optimal business production strategy and the true plant output must thus be minimised (Stephanopoulos and Ng, 2000). For example, a polyethylene reactor must be capable of producing different polymer grades. A transition from one grade to another may require several hours, which corresponds to a loss in prime production. Though a scheduling concern, transition may be necessary up to twice a week, resulting in financial losses between $10 000 to $50 000. Minimising transition losses is compounded by the process non-linearities (Piché et al., 2000).

Optimal operation is defined as the continuous, dynamic optimisation of plant operation using a perfect plant model (i.e., no process/model mismatch), thereby minimising a cost function, J, by adjusting the degrees of freedom. The scalar objective function, J, typically reflects the operating cost. Owing to process model uncertainty and inaccurate sensor inputs, optimal operation is generally not attainable. The discrepancy between the actual value for the cost function J and the global optimum for J, is defined as the process loss (Larsson & Skogestad, 2000). Larsson and Skogestad (2000) defined the concept of an acceptable loss as a process loss resulting from using a fixed vector of set points, without re-optimising in response to process disturbances (or the prevailing market conditions) within a specified time frame. Ideally, process loss should not result due to a trade-off between optimal economic operation and controllability, but only due to model and state information uncertainty. However, separating the optimisation and control layer is frequently necessary, particularly since the time constant of the regulatory control system is usually smaller than the minimum re-optimisation time frame. Provided the control response to the newly re-optimised steady state carries no cost penalty due to a poor response, the economic operation of the plant is determined by the newly calculated steady state operating point (Larsson & Skogestad, 2000).

Zheng et al. (1999) also emphasised the use of economic considerations when making plant-wide control structure decisions. Zheng et al. (1999) noted that the RGA for single unit operations differed significantly from the same unit operations in a plant- wide control scenario. Configurations that worked well for unit operations alone, did not necessary work well in the entire system. In addition, the multiple objectives of plant-wide control problem may not be weighted equally. When there is more than one objective function to be optimised, solutions exists where one objective cannot be further improved without sacrificing performance in other objectives. Such solutions are defined Pareto optimal and the set of all Pareto optimal solutions form the Pareto front. Generating a Pareto front based on varying weighted objectives may prove valuable, though computationally intensive. For example, most plant-wide control challenges require that a cost function, f, be minimised within a product purity equality constraint, h, as in equation 2-4:

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Within the specified range for product purity, the unit cost or added value may vary dramatically for highly non-linear processes. Successively relaxing the weight, ω2, on the product purity, generates a Pareto front. Without a full Pareto analysis, operating objectives must be ranked based on their total economic impact and thereby weighted accordingly to allow for calculation of a single scalar cost function (Stephanopoulos and Ng, 2000).

The production objectives of any process may vary significantly between two classes of economic objectives, viz. maximum production (seller's market) and lowest possible unit cost (buyer's market). Translating economic objectives into process control objectives entails finding a function of the process variables (i.e., state variable representations) in terms of the manipulated variables, which moves the state trajectory along an optimal path. Each possible operating region in the state space has an innate economic value. This may entail keeping a set of process variables constant in the absence of disturbances. However, during disturbances the control system should attempt to track the optimal economic trajectory as it relates to the state space. The overall operational (i.e., the economic objective) may be the minimisation of a scalar cost function, subject to operational constraints such as product purity, safety (e.g., maximum vessel pressures before mechanical failure) and controllability. A control system's robustness is determined by the controllability in the operating region of highest economic return (Morari et al., 1980).

applicable techniques for solving the plant-wide control problem has been attributed to the absence of a mathematical formulation and a clear statement of the objectives (Morari et al., 1980). The main goal of a control system design is to create a dynamic structure of process (i.e., measured) and manipulated variables, which meet production objectives continuously. Several process variables must be guided from an undesired state to a desired state, with an appropriate response during plant disturbances. Operational objectives vary based on management strategies determined by present and future economic outlooks. The optimal operating conditions change with the external disturbances and drifting process kinetics. Industrial practice shows that a changing production policy is feasible, though shifting the operation from one set of process conditions to another may require a change in the control structure. A fixed control structure may not assure a smooth transition from one operating region to another for better economic return (Morari et al., 1980).

In modern day real-time optimisation systems, the control system should be selected that yields the highest profit for a range of disturbances that may occur between each optimisation of the set point values (Skogestad, 2000a). In conventional control system development this implies finding a fixed control structure that is robust for a variety of disturbances, with only the set points and controller parameters changing with successive optimisations and adaptive control adjustments. A fluid control structure that changes along with the changes in the process and market conditions is more desirable.