G. van Straten

Towards user accepted optimal control of greenhouse climate.

Theoretically, using information about crop growth would allow the extension of present greenhouse control strategies towards a truly economic optimal control strategy. A brief survey is given of developments in the scientific literature. A full solution would require to consider the long-term crop development as well as all relevant short-term dynamics of the crop, the greenhouse and the external weather. Obstacles for the acceptance of such solutions are briefly discussed. One of the key factors is the lack of reliable crop development models for the wide variety of crops encountered in practice, and the need to leave part of the decision freedom in the hands of the grower. An analysis is given of simplified approaches resulting from integrating the crop equations over a day or more. The temperature integral concept, a specific example of such approach, is gaining popularity, despite the fact that it lacks exploitation of knowledge about the fast crop responses. The discussion leads to the concept of separation of responsibilities, where the short-term effects, including photosynthesis and evapo-transpiration, are handled by an automated model-predictive optimal controller, while the long-term effects are left to the grower, with support from a flexible decision support system based on crop models whenever they become available.

Optimal control of N-removal in ASPs

Abstract Optimisation of the operation strategy of N-removing activated sludge processes (ASPs) contributes to improved effluent quality and/or costs savings. This paper deals with the development of an aeration strategy yielding optimal N-removal in continuously mixed, continuously fed ASPs. First, optimal control theory is applied to the generally accepted ASM no.1 model (Henze et al. , 1987). This study reveals that, from an N-removal point of view, both alternating nitrification/denitrification and simultaneous nitrification/denitrification at limiting DO-levels might be optimal, depending on the uncertain oxygen half-saturation constants of autotrophic and heterotrophic biomass. Hence, talcing into consideration the risk of sludge bulking at limiting DO-levels, an alternating anoxic/aerobic strategy is favoured. A Receding Horizon Optimal Control (RHOC) strategy using NH 4 and NO 3 measurements is developed, enabling feedback control of the alternation between anoxic and aerobic phases with the explicit objective of optimal N-removal. Simple rules are given for straightforward tuning of this controller. The controller successfully passed several tests both in simulation and in application to a pilot plant continuously fed with presented domestic wastewater.

Analysis of endogenous process behavior in activated sludge

In this article, an autonomous four-compartment model that describes the endogenous respiration in an aerobic biodegradation process is proposed and analyzed theoretically. First, the multi-time scale of the systems behavior, to be taken into account in subsequent analyses, is emphasized. Then, an identifiability and observability study, given measurements of MLVSS (mixed liquor volatile suspended solids) and respiration rate, is performed for use under practical circumstances, such as in state and parameter estimation. It appears that the process is observable, but not fully identifiable. Hence, for the identification of some of the model parameters, additional measurements or experiments, also indicated here, have to be performed. Furthermore, it is shown that, under quasi-steady state conditions which, in general, appear shortly after initialization of an endogenous respiration experiment, the model can be reduced significantly. Finally, results of parameter estimation from available data are presented and discussed.

A comparison of NH4/NO3 control strategies for alternating activated sludge processes

Four control strategies for N-removal in alternating activated sludge plants (ASPs) are compared: 1. timer-based, 2. switching the aeration on/off when depletion of nitrate/ammonium is detected, 3. switching the aeration on/off when ammonium crosses an upper/lower-bound, 4. the newly developed adaptive receding horizon optimal controller (ARHOC) as presented in Lukasse et al. (1997). The comparison is made by simulating the controllers application to an alternating continuously-mixed activated sludge reactor preceded by a small anoxic reactor for predenitrification. The biological processes in the reactors are modelled by the activated sludge model no. 1. Realistic influent patterns, measured at a full-scale wastewater treatment plant, are used. The results show that three totally different controllers (timer-based, NH4-bounds based and ARHOC) can achieve a more or less equal effluent quality, if tuned optimally. The difference mainly occurs in the sensitivity to suboptimal tunings. The timer-based strategy has a higher aeration demand. The sensitivity of the ARHOC controller to sub-optimal tuning, known measurement time delays and changing plant loads is significantly less than that of the other controllers. Also its tuning is more natural and explicit.

Intra-Row Weed Control: A Mechatronics Approach

Abstract A fully automated system for mechanical weed control in a row of plants is described and discussed. The performance of the system is shown by laboratory experiments.

Analysis of endogenous process behaviour

In this paper an autonomous four-compartment model that describes the endogenous respiration in a aerobe biodegradation process is proposed and analysed theoretically. First the multi-time scale of the systems behaviour, to be taken into account in subsequent analyses, is emphasized. Then, an identifiability study, given measurements of MLVSS (mixed liquor volatile suspended solids) and respiration rate, is performed for use under practical circumstances such as in parameter estimation. It appears that the process is not fully identifiable. Hence, for the identification of some of the model parameters additional measurements or experiments, also indicated in the paper, have to be performed. Furthermore, it is shown that, under quasi-steady state conditions which in general appear shortly after initialization of an endogenous respiration experiment, the model can be reduced significantly.

l1-norm optimal control of N-removal in an activated sludge process

Abstract This paper presents an l 1 -norm optimal state feedback controller for two-dimensional linear time invariant (LTI) systems with decoupled dynamics and a single control input. The controller is successfully applied to the problem of N-removal in activated sludge processes, both in simulation and on a pilot plant fed with real municipal wastewater. It optimises the moments at which the plant’s aerators are switched on/off. Improvement of operation strategies for the process of N-removal from wastewater is an important topic due to tightening government legislations with the objective to protect the aquatic environment.

A Neuro-Fuzzy Approach to Identify LettuceGrowth and Greenhouse Climate

A hybrid neuro-fuzzy approach called the NUFZY system, which embeds fuzzy reasoning into a triple-layered network structure, has been developed to identify nonlinear systems. A set of membership functions at the input layer is partially linked with a layer of rules, using pre-set parameters. By means of a simplified centroid of gravity defuzzification method, the output becomes linear in the weights. Therefore, very fast estimation of the weight parameters can be achieved by using the orthogonal least squares (OLS) method, which also provides a method to efficiently remove the redundant fuzzy rules from the prototype rule base of the NUFZY system. In this paper, the NUFZY system is applied to identify lettuce growth and greenhouse temperature from real experimental data.Results show that the NUFZY model with the fast OLS training can perform quite well in predicting both lettuce growth and greenhouse temperature. In contrast to the mechanistic modeling procedures, the neuro-fuzzy approach offers an easier route and a fast way to build the nonlinear mapping of inputs and outputs. In addition, the resulting internal network structure of the NUFZY system is a self-explanatory representation of fuzzy rules. Under this frame, it is a perspective that one is able to incorporate the human knowledge in this approach, and, hopefully, to deduce any interpretable rules that describe the systems behavior.

Paradigms in greenhouse climate control: on hierarchy and energy savings.

Abstract Two major paradigms for greenhouse climate control are hierarchical control, where set points are transferred from some decision unit to a suitable low-level controller, and integrated control, where the control actions are obtained by optimising an explicit objective function. Results from both classes are quoted, with special emphasis on energy savings. In principle, providing optimised set point trajectories to a hierarchical controller could combine the advantages of both paradigms. Preliminary calculations are given suggesting that considerable losses with respect to the true optimum may occur.

Sensitivity of on-line RHOC of greenhouse climate to adjoint variables for the crop

Abstract The optimal control of greenhouse climate and crop cultivation is performed by two-time-scale decomposition. First the slow sub-problem is solved leading lo a seasonal pattern tor the crop adjoint variables associated to the assimilate buffer, and the fruit and leaf weights. The adjoint variables or co-states are then used to represent the marginal price of a unit of buffer, leaf and fruit in an on-line receding horizon control of the greenhouse climate. Comparing simulations using the dynamic co-slates to experimental results obtained with fixed co-states reveals that the on-line control is sensitive to the co-state trajectory. This suggests that it is advantageous to repeat the seasonal optimization from time to time to adjust to past weather and realized crop state.