Optimizing and controlling the operation of heat-exchanger networks

An Optimal-Control Scheme for Coordinated Surplus-Heat Exchange in Industry Clusters

Industrial plants organized in clusters may improve their economics and energy efficiency by exchanging and utilizing surplus heat. However, integrating inherently dynamic processes and highly time-varying surplus-heat supplies and demands is challenging. To this end, a structured optimization and control framework may significantly improve inter-plant surplus-heat valorization. We present a Modelica-based systems model and optimal-control scheme for surplus-heat exchange in industrial clusters. An industry-cluster operator is assumed to coordinate and control the surplus-heat exchange infrastructure and responsible for handling the surplus heat and satisfy the sink plants’ heat demands. As a case study, we use an industry cluster consisting of two plants with surplus heat available and two plants with heat demand. The total surplus heat and heat demand are equal, but the availability and demand are highly asynchronous. By optimally utilizing demand predictions and a thermal energy storage (TES) unit, the operator is able to supply more than 98% of the deficit heat as surplus heat from the plants in the industry cluster, while only 77% in a corresponding case without TES. We argue that the proposed framework and case study illustrates a direction for increasing inter-plant surplus-heat utilization in industry clusters with reduced use of peak heating, often associated with high costs or emissions.

Cooperative-feedback control

In this work a control structure capable of handling controllability problems, which emerge from the presence of constraints, and improve the performance of the system by coordinating the use of several manipulated variables is introduced. In this scheme, the primary manipulated variable is used to handle the transient response while the auxiliary manipulated variable is used to keep the primary manipulated variable away from saturation. These two manipulated variables are coordinated through a user-defined non-linear function, which decides when and how the control structure changes. Its parameters determine the interaction between both inputs and the steady state value of each manipulated variable. The effectiveness of the proposed scheme is illustrated in two simulation examples.

Flexible-structure control: a strategy for releasing input constraints

In this paper output unreachability under input saturation phenomenon is studied: under a large disturbance or setpoint change, the process output may never reach the set point even when the manipulated variable has driven to saturation. The process output can be brought back to the set point only by activating an auxiliary manipulated variable. A new control structure for designing and implementing a control system capable of solving this problem is proposed by transferring the control from one variable to another and taking into account the different dynamics involved in the system. The control structure, called flexible-structure control due to its ability to adapt the control structure to the operating conditons, is a generalization of the split-range control. It can be summarized as two controllers connected through a piecewise linear function. This function decides, based on the value of one manipulated variable, when and how the control structure changes. Its parameters control the interaction between both manipulated variables and leave the capability for handling the balance between control quality and other goals to the operator.