A new plant growth system rig based on thermodynamic solar energy: a study for energy efficiency assessment

At the present there is a high pressure toward the improvement of all production processes. Those improvements can target distinct factors along the production chain. In particular, and due to recent tight energy efficiency policies, those that involve energy efficiency. As can be expected, agricultural processes are not immune to this tendency. Even more when dealing with indoor productions. In this context, this work presents an innovative system that aims to improve the energy efficiency of a trees growing platform. This improvement in energy consumption is accomplished by replacing an electric heating system by one based on thermodynamic panels. The assessment of the heating fluid caudal and its temperature was experimentally obtained by means of a custom made scaled prototype whose actuators status are commanded by a Fuzzy-based controller. The obtained results suggest that the change in the heating paradigm will lead to overall savings that can easily reach 60% on the energy bill.

Model predictive control applied to a supply chain management problem

Supply chains are ubiquitous in any commercial delivery systems. The exchange of goods and services, from different supply points to distinct destinations scattered along a given geographical area, requires the management of stocks and vehicles fleets in order to minimize costs while maintaining good quality services. Even if the operating conditions remain constant over a given time horizon, managing a supply chain is a very complex task. Its complexity increases exponentially with both the number of network nodes and the dynamical operational changes. Moreover, the management system must be adaptive in order to easily cope with several disturbances such as machinery and vehicles breakdowns or changes in demand. This work proposes the use of a model predictive control paradigm in order to tackle the above referred issues. The obtained simulation results suggest that this strategy promotes an easy tasks rescheduling in case of disturbances or anticipated changes in operating conditions. © Springer International Publishing Switzerland 2017

Brain emotional learning based control of a SDOF structural system with a MR damper

This paper describes the application of a Brain Emotional Learning (BEL) controller to improve the response of a SDOF structural system under an earthquake excitation using a magnetorheological (MR) damper. The main goal is to study the performance of a BEL based semi-active control system to generate the control signal for a MR damper. The proposed approach consists of a two controllers: a primary controller based on a BEL algorithm that determines the desired damping force from the system response and a secondary controller that modifies the input current to the MR damper to generate a reference damping force. A parametric model of the damper is used to predict the damping force based on the piston motion and also the current input. A Simulink model of the structural system is developed to analyze the effectiveness of the semi-active controller. Finally, the numerical results are presented and discussed.