This work describes the application of a Model Predictive Control (MPC) strategy to the acid abatement process in a Waste to Energy (WtE) plant. By exploiting real closed-loop operational data, collected from an Italian WtE plant, black-box methods are applied to identify suitable prediction and simulation models for the development and validation of the learning-based MPC strategy. Simulation results under real operation conditions show an increment in cost-effectiveness of reagent usage in the abatement process, with potential savings of up to 24.5 tons/year. Furthermore, it also allows increasing the effectiveness in reference tracking and the compliance with stricter emission limits.

The present paper considers the problem of planar motion planning for environments with partially unknown obstacles for a robot with tether connection and missions with multiple target positions. The presence of a tether increases the complexity of the already challenging problem of dynamic motion planning by introducing additional feasibility constraints, i.e. not entangling the cable in any of the obstacles within the working space. For a given mission, the developed algorithm finds a closed trajectory such that after its execution the cable can be retrieved without difficulties. It combines the dynamic path-planning capabilities of the RRTX(Rapid-exploring Random Tree X) method with a geometric approach that efficiently identifies feasible paths that satisfy all the criteria imposed by the problem. Extensive simulation tests demonstrate the validity and reduced computational complexity of the proposed solution.

In numerous applications across all science and engineering areas, there are optimization problems where both the objective function and the constraints have no closed-form expression or are too complex to be managed analytically, so that they can only be evaluated through experiments. To address such issues, we design a global optimization technique for problems with black-box objective and constraints. Assuming Lipschitz continuity of the cost and constraint functions, a Set Membership framework is adopted to build a surrogate model of the optimization program, that is used for exploitation and exploration routines. The resulting algorithm, named Set Membership Global Optimization with black-box constraints (SMGO-Δ), features one tunable risk parameter, which the user can intuitively adjust to trade-off safety, exploitation, and exploration. The theoretical properties of the algorithm are derived, and the optimization performance is compared with representative techniques from the literature in several benchmarks. An extension to uncertain cost/constraint function outcomes is presented, too, as well as computational aspects. Lastly, the approach is tested and compared with constrained Bayesian optimization in a case study pertaining to model predictive control tuning for a servomechanism with disturbances and plant uncertainties, addressing practically-motivated task-level constraints.

Modeling heating phenomena for industrial applications is crucial for both the optimization and performance control of these systems. In this work, we focus on creating a modular digital twin for modeling thermoforming systems based on a lumped parameter model. This paper discusses and demonstrates the validity of a complete model-based digital twin and data integration system to analyze a direct heating system with ceramic heating elements. A cascade model describing the system’s main features is presented: the heaters, the view factor explaining the effect of the heaters on the sheet, and finally, a heating model of a flat polymer sheet. Validation of the model is then done using finite element software. The proposed mathematical model has low complexity and is useful in the development of improved control strategies, optimization of geometric parameters, analysis of disturbance reduction techniques, heater characterization, and sensory system definition.

Nowadays, electric vehicles represent one of the most significant chances to reduce the pollution production rate. Unfortunately, in electric motors, the efficiency decreases by the relationship between speed proposed by the driver and the torque required by the vehicle. Those parameters can be estimated in order to make an efficiency optimization based on present and future road/weather conditions. Regrettably, this kind of control (optimal control) requires a model with low compilation time. Since bicycle motorcycle has nonlinearities, in this article, a state reduced linear dynamic model able to reproduce the behavior of a TWEV by more than one minute will be proposed. The model is oriented to an optimal controller in energetic field, for this reason, the most significant states are longitudinal speed to be used with the input torque to calculate the efficiency of the electric motor and the Yaw angle to creates constraints over the trajectory that has to be covered. After the model is proposed, its accuracy is tested by a comparison with a numerical iteration software. Finally, a sensitivity test is made in order to determine the behavior of the error according to the friction coefficients of the rear and front pneumatics.

The energy required by a two wheels electric vehicle (TWEV) to complete a trip is lower than common electric cars or internal combustion vehicles. However, there are considerable losses along the electric driving chain. Those losses added to a limited energy storage cause an impact over the TWEV autonomy. This appears to be the main factor, which limits the large-scale market penetration of TWEV. This paper aims to analyze the multiphysic behavior of the complete power-chain in order to study its effect on its energetic losses. Even when many dynamics model oriented to hardware design approach can represent the come multiphysic behavior of one or two elements of the power train, the approach proposed in this paper presents a balanced representation of all power chain able to be used in real-time optimization. This study will help to improve the capabilities of an onboard TWEV efficiency estimator system which uses a longitudinal force model. As a conclusion, the error of autonomy estimation is compared with thermal considerations and without them according to different operating points.