This paper proposes some solutions to optimize the Butterworth filter from the structure of plug-in type repetitive controllers, as well as the use of the Bessel filter, which offers a simpler way of choosing the anticipatory factor from the repetitive controller structure. The comparative analysis of the two types of filters was made on the basis of three criteria: 1) the standard deviation of the error in the control loop, where the first type of filter proved to be superior; 2) ease of obtaining the optimal value of the anticipative parameter, in which case the Bessel filter is preferred; 3) sensitivity to changes in the filter parameters and/or process model, where the second filter is also preferred

Wastewater treatment processes are used to reduce the amount of polluting substances in waste water resulting from human or industrial consumption. Afterwards, the water is discharged in lakes, rivers, seas, therefore it is important that these systems have an increased efficiency when it comes to treating the waste water. In this paper, we consider a nonlinear wastewater treatment system with four states and one input. We introduce an optimal control problem with state - input constraints, and a corresponding model predictive control scheme and we solve it using ACADO Toolkit. Our main reasons for using the ACADO Toolkit are that it is Open Source, it has a user friendly MATLAB interface and has the advantage of being self - contained (only needs a C++ compiler). We also consider a model predictive control scheme based on the linearization of the nonlinear system at each step, and we solve it using a standard quadratic program solver. From simulations we observe that both approaches produce a similar closed loop behavior, but the linearization based approach is faster in terms of CPU time.

Microalgae are attractive in wastewater treatment applications due to their capacity of growing on inorganic nutrients (e.g., nitrogen and phosphorus salts) while their biomass is used to produce biodiesel, biogas or even bioethanol. The photosynthetic growth is the most attractive mechanism, but it brings additional limitation to the open ponds or the Photobioreactors (PBRs) in which they grow. The light is thus the main factor that drives the growth of microalgae and, with the development of commercial efficient LED light sources, the indoor cultures can become more efficient. The use of artificial light creates a new class of controllers, the lumostas, by which the incident light intensity is manipulated. In this paper, an alternative proposal based on PID control acting on Biomass algae desired profile is proposed. It is seen that the generated manipulated incident light generates optimal profiles for he biomass yield on light ratio. Also, better, more efficient batches that the ones based on model prediction over all the batch are obtained. This is a preliminary result that is to be extended with biomass observers. Also, in future work, the tuning of the PID controller in a more automated way will be addressed.

The present study aimed to develop a model that simulates the growth of Porphyridium purpureum, which explores the dependency of biomass concentration to light availability, through coupling of the radiative model and the kinetic growth of microalgae biomass. The desiderate was to fit the model with the biomass concentration data. The offline experimental dry mass data were used for the parameter estimation procedure. It was observed that it is not necessary to estimate more than one parameter to fit the model which results in a unidimensional optimization procedure of the error function. Future work will be focused on estimating the parameters that will fit the global photosynthetic growth model on the online data of dissolved oxygen, dissolved carbon dioxide and pH.

This paper deals with the increasing of the operation efficiency of a wastewater treatment plant by reducing the level of pollutant concentration in the effluent. In essence, the main goal is the reduction of the concentration of organic substrate, Model Predictive Control algorithm being used for this, considering as a model of the wastewater treatment process a neural network model based on a simplified mathematical model of the 4th order. For the implementation of the control algorithm, two neural networks were trained. In the first version of the neural network, it was considered that all 4 state variables are measurable and in the second version, only the organic substrate was considered measurable. Regarding the training of the two neural networks, good results were obtained in the case of both versions. Instead, the control algorithm gave better results when the second neural network was used (lower values of the substrate, lower variations of the aeration rate, which means a lower cost of operation).

To increase the biogas production, the anaerobic digestion process requires advanced control tools. However, in order to achieve an advanced control of the anaerobic digestion process, two constraints must be taken into account: the need for available plants for experimentation and the need to reduce costs. In this paper, it is proposed to develop a test platform for the anaerobic digestion process using the Hardware in the Loop Simulations principle. The emulation of the anaerobic digestion process is ensured by a computer that communicates with a data acquisition board, and the process control is ensured by a Programable Logic Controller. Such a platform is effective because it allows testing in extreme situations, which in the case of environmental processes is very difficult. The platform also includes a cloud computing component for advanced data processing.

The paper, elaborated within the framework of the INTELSIS research project, presents the current status in achieving a photovoltaic emulator. A DC/DC boost converter is connected between an energy source (PV panel or DC sources) and a battery bank or resistive load. In order to have independent experiments on weather conditions, a PV emulation circuit is used for testing the maximum power point tracking (MPPT) block. The paper presents a simple, effective and configurable emulation circuit that uses DC power supplies in order to approximate the U/I characteristic of a real PV panel, from a small number of configurable segments. Although the implementation of a MPPT algorithm in the associated hardware seems a trivial task, subtle problems greatly affect the efficiency of the system. The MPPT algorithm operation is validated on a PV emulator and also with a PV panel.

Disturbance attenuation is often recognized as the primary concern of a control system. Regulation of the operating conditions is the usual task work to be pursued by a feedback controller. However, much of the academic works almost concentrate on set-point experiments for controller evaluation. Even those that explicitly concentrate on the disturbance attenuation problem, usually concentrate on input load disturbances. They exclude the specific, but usually not considered in the literature, case when the path from the disturbance to the controlled variable is different to the path from the controller output to the controlled variable. There are some process units such as heat exchangers or distillation columns where this situation may be encountered. However actual feedback controllers, usually of PI/PID type, do not take into account this situation. The point raised in this paper is that an increment of performance can be obtained if the information regarding the disturbance dynamics is included in the PI/PID design stage.

This work faces the problem of frequency deviation in microgrid systems. The considered microgrid includes renewable energy sources such as wind and solar photovoltaic. As long as these sources provide an irregular power supply or there is a sudden change in the system load, the power system frequency deviates. In order to compensate such deviations, alternative, conventional energy sources should be commanded in order to provide the corresponding power deficit. In this paper a very simple and of common industrial practice control approach such as the Internal Model Control based on first order plus time delay models is proposed within an event-based framework. As the commanded energy sources are based on fuel consumption, the control usage has to be maintained at low levels. It is shown that the event-based approach is able to provide accurate frequency deviation control with very low movements for the control signal. Time domain simulations show the effectiveness of the approach as compared with other more sophisticated controllers already proposed in the literature.

In this paper, a One-Degree-of-Freedom PI controller is optimized using the model reference tuning approach for a Second Order Inverse Response and Dead Time Process operating as a servo control. In addition, a graphic user interface tool that computes the PI optimized controller parameters is presented, also showing the response of the control system operating as a servo-control (the optimized one) and the associated response for the regulatory-control case.

The control of low power systems, which include renewable energy sources, a local network, an electrochemical storage subsystem and a grid connection, is inherently hierarchical. The lower level consists on the control systems of wind sources (power limitation at rated value in full load regime and energy optimization in partial load regime) and photovoltaic (energy conversion optimization). The control problem at the higher level is treated in this paper and aims at generating the control solution for the energetic transfer between the system components, given that the powers of the renewable energy sources and the power in the local network are random variables. For the higher level, the paper proposes a mixed performance criterion, which includes an energy sub-criterion concerning the costs of electricity supplied to local consumers, and a sub-criterion related to the lifetime of the battery. It was defined an algorithm to control the energy transfer in the system, implemented by using fuzzy techniques and a deficit/surplus prediction of energy in the system.