Optimal substrate feed control of biogas plants is a complex and challenging task due to the nonlinearity of the anaerobic digestion process, which produces biogas from biodegradable input material. In this paper a nonlinear model predictive control (NMPC) scheme is applied to optimally control the substrate feed of an agricultural biogas plant. The implemented algorithms are investigated in a simulation study using a validated simulation model of a full-scale biogas plant. Process states are estimated using a recently developed state estimator. Results show that this approach is very feasible providing the plant operator with a gain of 550 per day compared to previous operation.

 A nonlinear predictive control strategy (NEPSAC) is formulated for a continuous bioreactor characterized by severe non-linearity and parameter time-variance, with the control objective being to regulate the fermenter operation near the optimum productivity. Robustness towards the unpredictable time-variance, which comes from cell-mass growth, is guaranteed by disturbance filter design. The dilution rate and feed substrate concentration are considered as manipulated inputs, while the state variables are the biomass, substrate and product concentrations. Results obtained with robust NEPSAC are presented and compared with those obtained with other model predictive control and various linearization based strategies.

 People usually spend most of the time inside buildings. Therefore, it is necessary to reach a tradeoff between users comfort and energy saving. The use of appropriate control strategies can highly contribute to this purpose. This paper presents a practical nonlinear model predictive control strategy, that allows to obtain a high thermal comfort level optimizing the use of an HVAC (Heating, Ventilation and Air Conditioning) system. Simulation results obtained from the application of this strategy to a characteristic room of the CDdI-ARFRISOL-CIESOL building are included and commented.

 Distributed energy resources (DER) are expected to play a critical role in the near future due to a number of advantages they have compared to conventional centralised power systems. DER systems typically involve many components that combine different technologies for producing and storing electrical energy and heat. Thus, operational optimisation and control of DER systems is a challenging task. In this work, the Model Predictive Control (MPC) rolling horizon approach is adopted to design a control strategy for DER systems. The proposed approach is not limited to single households but addresses DER systems in the neighbourhood level, taking into account the possibility of energy exchange among neighbouring households.

 Over the last years, a number of publications were written about Model Predictive Control (MPC) on industrial Programmable Logic Controllers (PLC). They focussed on explicit MPC strategies to provide a fast solution. When sufficient time is available to solve a classic MPC problem, an online solution to the corresponding Quadratic Problem (QP) can be provided. This paper investigates the use of an online quadratic programming solver to exploit MPC on a PLC. This will be illustrated with the classic Hildreth QP algorithm and qpOASES, a recently developed online active set strategy. These algorithms will be investigated on a MISO system.