Control structure design and dynamics modelling of the organic Rankine cycle system

Integration of Numerical Simulation and Control Scheme for Energy Conservation of Aluminum Melting Furnaces

“…Pairing manipulated variables and controlled variables for structuring a partially decentralized model is important for minimizing interactions. Here, a criterion based on relative normalized gain array (RNGA) [26]- [28] is introduced to determine the partially decentralized model structure of MPC for F1 furnace. The lists of manipulated variables, controlled variables and disturbance variables in the two-layer MPC are shown in Table 2-4, and the priorities of the controlled variable constraints classified into three types: safety constraints, quality constraints and economic constraints ordered from high to low are shown in Table 5.…”

Optimal tuning of model predictive controllers for organic Rankine cycle systems recovering waste heat from heavy-duty vehicles

“…The authors found that an expander speed controller regulating for an optimized temperature of the working fluid at expander inlet could achieve the highest overall efficiency of 6.6 %. Jolevski et al [17] studied the interactions among the ORC actuators (pump speed, turbine throttle valve and coolant flow rate) and the controlled variables (pressure and temperature at turbine inlet and condenser outlet) by using non-square and dynamic square relative gain array techniques. Once the main interactions were found, singleinput-single-output PI controllers were designed and tested.…”