Long term solar radiation forecast using computational intelligence methods

The point prediction quality is closely related to the model that explains the dynamic of the observed process. Sometimes the model can be obtained by simple algebraic equations but, in the majority of the physical systems, the relevant reality is too hard to model with simple ordinary differential or difference equations. This is the case of systems with nonlinear or nonstationary behaviour which require more complex models. The discrete time-series problem, obtained by sampling the solar radiation, can be framed in this type of situation. By observing the collected data it is possible to distinguish multiple regimes. Additionally, due to atmospheric disturbances such as clouds, the temporal structure between samples is complex and is best described by nonlinear models. This paper reports the solar radiation prediction by using hybrid model that combines support vector regression paradigm and Markov chains. The hybrid model performance is compared with the one obtained by using other methods like autoregressive (AR) filters, Markov AR models, and artificial neural networks. The results obtained suggests an increasing prediction performance of the hybrid model regarding both the prediction error and dynamic behaviour

Fuzzy control of a water pump for an agricultural plant growth system

At the present time there is a high pressure toward the improvement of all the production processes. Those improvements can be sensed in several directions in particular those that involve energy efficiency. The definition of tight energy efficiency improvement policies is transversal to several operational areas ranging from industry to public services. As can be expected, agricultural processes are not immune to this tendency. This statement takes more severe contours when dealing with indoor productions where it is required to artificially control the climate inside the building or a partial growing zone. Regarding the latter, this paper presents an innovative system that improves energy efficiency of a trees growing platform. This new system requires the control of both a water pump and a gas heating system based on information provided by an array of sensors. In order to do this, a multi-input, multi-output regulator was implemented by means of a Fuzzy logic control strategy. Presented results show that it is possible to simultaneously keep track of the desired growing temperature set-point while maintaining actuators stress within an acceptable range.This work is financed by the ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT -Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project “*FCOMP-01-0124-FEDER-037281*

Real-time control of a laboratory heat exchanger using the particle swarm optimisation algorithm

In the past decade, evolutionary based algorithms have been a popular research theme in many disciplinary areas like control systems. Although, due to the computational load required, this type of algorithms usually are applied off-line. In this paper, a stochastic search algorithm known as particle swarm is used as an optimisation tool for on-line control of a custom made laboratory thermodynamic system

Greenhouse air temperature control using the particle swarm optimisation algorithm

The particle swarm optimisation algorithm is proposed as a new method to design a model based predictive controller subject to restrictions. Its performance is compared with the one obtained by using a genetic algorithm for the environmental temperature control of a greenhouse. Controller outputs are computed in order to optimise future behaviour of the greenhouse environment, regarding set-point tracking and minimisation of the control effort over a prediction horizon of one hour with a one-minute sampling period

Hardware-in-the-loop control using the particle swarm optimisation

In the last two decades, evolutionary based algorithms have proved to be an important tool in solving optimisation problems in many disciplinary areas namely in control system design. However one of its limitations, for some type of applications, is the usually high computational load required, which restricts its use for on-line control. This paper proposes the use of a stochastic search algorithm, known as particle swarm, as an optimisation tool for an on-line predictive control of a custom made thermodynamic system. Preliminary results are presented

Greenhouse air temperature modelling

This paper describes two implementation approaches for modelling the air temperature of an automated greenhouse located in the campus of the University of Trás-os- Montes e Alto Douro. Linear models, based in the discretization of the heat transfer physical laws, and non-linear neural networks models are used. These models are describes as functions of the outside climate and control actions performed for heating and cooling. Results are presented to illustrate the performance of each model in the simulation and prediction of the greenhouse air temperature. The data used to compute the simulation models was collected with a PC-based acquisition and control system using a sampling time interval of 1 minute.The authors appreciate the support of the Portuguese Foundation for Science and Technology (FCT) through the project MGS/ 33906/2000

Barn owl feathers as biomonitors of mercury: sources of variation in sampling procedures.

Given their central role in mercury (Hg) excretion and suitability as reservoirs, bird feathers are useful Hg biomonitors. Nevertheless, the interpretation of Hg concentrations is still questioned as a result of a poor knowledge of feather physiology and mechanisms affecting Hg deposition. Given the constraints of feather availability to ecotoxicological studies, we tested the effect of intra-individual differences in Hg concentrations according to feather type (body vs. flight feathers), position in the wing and size (mass and length) in order to understand how these factors could affect Hg estimates. We measured Hg concentration of 154 feathers from 28 un-moulted barn owls (Tyto alba), collected dead on roadsides. Median Hg concentration was 0.45 (0.076-4.5) mg kg(-1) in body feathers, 0.44 (0.040-4.9) mg kg(-1) in primary and 0.60 (0.042-4.7) mg kg(-1) in secondary feathers, and we found a poor effect of feather type on intra-individual Hg levels. We also found a negative effect of wing feather mass on Hg concentration but not of feather length and of its position in the wing. We hypothesize that differences in feather growth rate may be the main driver of between-feather differences in Hg concentrations, which can have implications in the interpretation of Hg concentrations in feathers. Finally, we recommend that, whenever possible, several feathers from the same individual should be analysed. The five innermost primaries have lowest mean deviations to both between-feather and intra-individual mean Hg concentration and thus should be selected under restrictive sampling scenarios

On-line control using the particle swarm optimisation algorithm

In the last two decades, evolutionary based algorithms have proved to be an important tool in solving optimisation problems in many disciplinary areas, namely in control system design. However one of its limitations for some type of applications is the usually high computational load required, which restricts its use for on-line control. This paper proposes the use of a stochastic search algorithm, known as particle swarm, as an optimisation tool for an on-line model predictive control of a custom made laboratory thermodynamic system. Preliminary results are presented

Rotational speed control of multirotor UAV's propulsion unit based on fractional-order PI controller

In this paper the synthesis of a rotational speed closed-loop control system based on a fractional-order proportional-integral (FOPI) controller is presented. In particular, it is proposed the use of the SCoMR-FOPI procedure as the controller tuning method for an unmanned aerial vehicle’s propulsion unit. In this framework, both the Hermite-Biehler and Pontryagin theorems are used to predefine a stability region for the controller. Several simulations were conducted in order to try to answer the questions – is the FOPI controller good enough to be an alternative to more complex FOPID controllers? In what circumstances can it be advantageous over the ubiquitous PID? How robust this fractional-order controller is regarding the parametric uncertainty of considered propulsion unit model?info:eu-repo/semantics/publishedVersio

Automation and control of the SORTEGEL wastewater plant

Food Processing Industries produce large amounts of wastewater with high environmental impact. Due to the high content of suspended matter and inadequate pH value of the wastewater, national laws prohibit direct discharges of the influent to the environment. This work describes the design and operation of a wastewater treatment plant installed in the Sortegel food-processing company located in Sortes, Portugal. This industry uses the water collected from groundwater wells to process raw materials and to wash the equipments, being the volume of wastewater produced season dependent (80 to 300m3/day). Results show that the implemented wastewater treatment plant and the automation solutions generate treated effluents that comply with the Portuguese legislation