A Robust Adaptive Dead-Time Compensator with Application to A Solar Collector Field

This paper describes an easy-to-use PI controller with dead-time compensation that presents robust behaviour and can be applied to plants with variable dead-time. The formulation is based on an adaptive Smith predictor structure plus the addition of a filter acting on the error between the output and its prediction in order to improve robustness. The implementation of the control law is straightforward, and the filter needs no adjustment, since it is directly related to the plant dead-time. An application to an experimentally validated nonlinear model of a solar plant shows that this controller can improve the performance of classical PID controllers without the need of complex calculations.Ministerio de Ciencia y Tecnología TAP95-37

Simple prefilter design in GPC for a wide class of industrial processes

This paper presents a simple design method for improving the robustness of the GPC using the T polynonial as a filter. Although different methods have been proposed in literature, the one presented here not only proposes a choice of the polynomial, but differs from the others in the way that the polynomial is used to filter the predktions. The method is valid for a wide range of processes commonly found in industry, those that can be described by means of a static gain, time constant and dead-time and has special interest when the process has uncertainties in the dead-tim

Split-range control for improved operation of solar absorption cooling plants

This paper proposes the first application of a split-range control technique on a concentrating solar collector to improve an absorption plant production. Solar absorption plants have solar power availability in phase with cooling demand under design conditions. Thus, it is a powerful cooling technology in the context of renewable energy and energy efficiency. These plants need control systems to cope with solar irradiance intermittency, reject irradiation disturbances, manage fossil fuels backup systems and dump closed-loop thermal-hydraulic oscillations. In this work, control techniques are proposed and simulated in an absorption plant in Spain. The plant consists of a concentrating Fresnel solar collector connected to an absorption chiller. The objectives are to operate with 100% renewable solar energy and avoid safety defocus events while reducing temperature oscillations and control actuators effort. Firstly, the current available plant controllers are defined, then two modifications are proposed. The first modification is a split-range controller capable of manipulating both flow and defocus of the Fresnel collector, the second modification is a PI controller to substitute the original chiller on-off controller. The results compare, through validated models, the different control systems and indicate that using both proposed controllers reduces 94% of the sum of actuators effort and 43% of the integral of absolute set-point tracking error compared to the plant's factory pre-set controllers. The suggested controllers increase 66% of energy production and 63% of exergy production. Besides, the split-range technique can be extended to any concentrating solar collector control.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Finance Code 001Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) 304032/2019–0Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (AEI) PID2019-104149RB-I00/10.13039/501100011033Consejo Europeo de Investigación (ERC) OCONTSOLAR 78905

An online assessment & feedback approach in project management learning

This work presents an online system to facilitate the assessment and feedback in project management education. Students are involved in real-world engineering projects in order to promote professional project management learning. Thus, students share an experience in executing and managing projects and are able to put into practice different skills and competences that a project member should possess in the development of a project. The proposed system considers competence assessment through different pieces of evidence that are pertinent to each assessed competence. Information from the three main actors in learning activities (teacher, peer, and learner) is collected by means of specifically developed online forms. All the gathered evidences are considered in a weighted integration to yield a numerical assessment score of each competence that is developed for each student. Furthermore, three different types of feedback are implemented and provided several times in order to promote and improve students' learning. Data analysis from a specific academic course suggest that the presented system has a positive impact on students' academic performance. © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

Bioremediation of waste water to remove heavy metals using the spent mushroom substrate of Agaricus bisporus

The presence of heavy metals in waste water brings serious environmental pollution that threatens human health and the ecosystem. Bioremediation of heavy metals has received considerable and growing interest over the years. Thus, this paper presents the use of the Spent Mushroom Substrate (SMS) of Agaricus bisporus cultivation as a bioremediating agent to remove heavy metals that are present in industrial waters. These metals include chromium, lead, iron, cobalt, nickel, manganese, zinc, copper and aluminium. In particular, this study analyses the performance of SMS bioreactors with different groups of heavy metals at various concentrations. Between 80% and 98% of all contaminants that were analysed can be removed with 5 kg of SMS at hydraulic retention times of 10 and 100 days. The best removal efficiencies and longevities were achieved when removing iron (III), nickel and cobalt from contaminated water at a pH of 2.5. These results suggest that SMS can successfully treat waste water that has been contaminated with heavy metals

Advanced predictive system using artificial intelligence for cleaning of steel coils

This paper presents a system based on data mining and statistical modelling tools that permits the prediction of the development of oxide scale defects in high quality flat products after the steel industrys hot strip mill process (HSM), but before the coil becomes processed on the pickling line (PL). The economic impact of the improvement provided by such a system can be valued at several million US dollars per year, because it makes it possible to downgrade materials at an early stage, avoiding additional processes like coating, etc. It also enables the speed of the PL, which is usually seen as a bottleneck in these facilities, to be increased. The learning process of the model presented here is based on automatic surface-inspection systems, as well as processing parameters at the HSM and PL to capture the essentials of the cleaning process itself, and also the main factors in scale production. The system proposed currently which is configured as a multi-agent system, is the first for this particular purpose, although the steel industry uses many other models and systems to predict other properties (e.g., mechanical properties) or the best operating parameters (e.g., forces, temperatures) for processe