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The salt tolerance of crops is commonly expressed in descriptive parameters such as threshold or 50%-yield soil salinity and shape parameters describing the yield curve. Estimation by visual or simplified ordinary least squares (OLS) regression methods has multiple issues: parameter bias due to uncertainty in soil salinity, lack of independent estimates of the reference yield, questionable robustness of the threshold parameter and missing information about uncertainty and correlation of the parameter estimates. Here, we present a comprehensive OLS method together with an analysis of its statistical properties to alleviate and overcome such issues, on the basis of a numerical experiment that mimics observed yield responses to saline groundwater across a range of salinities in the experimental test facility Salt Farm Texel. The results indicate under which experimental conditions bias is not a major problem. The method allows estimation of the zero-observed-effect yield from the data, which is relevant to agricultural practice. Estimates for zero-observed-effect yield and threshold ECe are negatively correlated, underlining the difficulty of obtaining reliable threshold values. The estimated confidence regions are reliable and robust against soil salinity uncertainty, but large observation error jeopardizes the confidence intervals, especially for the slope parameter. Data uncertainty alone can be responsible for substantial differences from experiment to experiment, providing a partial explanation for the wide variety in reported parameters in the literature, and stressing the need for long-term repetitions. Given the lack of robustness of the threshold parameter, we propose to adopt the 90%-yield EC (ECe90) as tolerance parameter. Its confidence bounds can be obtained from a simple reformulation of the original models. We also present uncertainty ellipses as a suitable tool to unite multiple-year estimates. The method is offered as a solid and generic basis for reliable assessment of the cultivation potential of varieties and crops on salt-affected soils.

Having salt-tolerant potatoes is of paramount interest to farmers in salt affected areas, but reliable cultivar-specific parameters on salt tolerance are lacking. To address this issue existing field data on tuber yield on sandy soil at six levels of saline irrigation (0.5, 4, 8, 12, 16, 20 dS m−1) of 13 varieties for which data in two or more consecutive years were available, were analysed year-by-year with the method developed earlier. The method provides estimates of the zero-observed-effect yield (Y0), and two typical salt tolerance parameters, - i.e., a characteristic salinity level and a decline parameter –, as well as information about the uncertainties and correlations between these estimates. The results indicate that all varieties have a similar lethal soil salinity (20–24 dS m−1). However, both yield Y0 as well as salt tolerance parameters differ among cultivars, but for a single variety the estimates vary year by year, and have large uncertainties, underlining the difficulty to obtain robust parameters from single year experiments. The annual variety also hampers the discrimination between varieties. To remedy this, the data from multiple years were united in a single analysis by introducing another – unknown, annually varying - factor that is limiting the yield in the trials. Two ways to describe co-current limitations often used in models were tested. In contrast to the minimum rule, the multiplicative rule is found to provide an acceptable description of the observed yields over all years. This results in a single set of salt tolerance parameters with a narrower uncertainty bound than from single year estimation. It shows that with due account of uncertainties, field tests can be used to identify relatively salt tolerant cultivars, while accounting for between-year yield differences. Most potato cultivars have an ECe90 of about 4–5 dS m−1, but for some it is roughly double, while maintaining good yield, suggesting that these varieties are good candidates for salt adapted agriculture.

Increasing variability in energy-saving equipment and systems in the greenhouse industry raises the question of how to best utilize the various equipment in such a setting. The development of adequate solutions for deployment and control of this diversity of equipment has not kept pace with the innovations in the greenhouse industry. In earlier work a two-step dynamic optimization framework was developed, where in step one energy demand for heating and cooling is optimized within the climate constraints set by the grower, and in step two energy costs are minimized of alternative equipment use to satisfy that demand. Here the aims are: (1) to develop step two; (2) to illustrate the potential cost savings of both steps by comparing optimization results with real-life data from one specific grower, as a benchmark. The energy equipment of a 4 ha semi-closed greenhouse was optimized on a daily basis using dynamic optimization for a period of one year. Predefined heating, cooling, and electricity demand patterns computed from available grower data served as input, together with realized prices for gas and electricity. The installed equipment contained a boiler, a CHP (combined heat and power installation), short term buffers for high and low temperature heat and cold water storage, a heat pump, an aquifer for long term heat and cold storage and cooling towers. Cooling towers are a new element in the field of greenhouse energy optimization. The results show that cost optimization of the energy system is feasible and beneficial. Energy cost savings of 29% were obtained for the optimized situation as compared to the real situation at the grower. All available equipment was utilized in the optimal situation. The results show that trading of electricity and short-term forecasting of gas and electricity prices in combination with dynamic optimization has a high potential for cost savings in horticultural practice. Dynamic optimization pointed to a higher share of sustainable energy in the energy budget.

The design of greenhouse environmental control involves simulations under various climate and system configuration alternatives. These off-line model-based simulations cannot use online measured greenhouse conditions (temperature, humidity) and must rely on historical weather data and on well-established greenhouse and crop properties. An important element of such simulations is the partitioning of the dissipated energy into sensible and latent heat, which can be achieved in different ways. Specifically, the rate of evapotranspiration (ET) may be obtained from 1) knowledge of the aerodynamic and stomatal canopy resistances, 2) estimating ET from atmospheric conditions and control settings, or 3) estimating the Bowen (sensible to latent heat) ratio in the ventilation stream (VBR) from the atmospheric conditions. The objective of this study is to compare the simulated behaviour of a tomato greenhouse, based on these three quite different approaches, both among themselves and against a full-year data set from a commercial Dutch cherry-tomato greenhouse. Using a quasi-steady-state simulation-optimization program with hourly time steps, trajectories of indoor conditions (temperature, humidity, CO2 concentration), control actions (heating, ventilation, CO2 enrichment, thermal screen), yield and ET were obtained, and their monthly averages presented. At the monthly time scale the simulated results are very similar among themselves and to those observed, with the exception that the simulated summer CO2 concentrations, and hence fruit yield, are higher than those observed, possibly due to the optimization inherent in the simulations. The considerable VBR discrepancy between the three ET formulations during autumn is due to low nighttime ET, but this has a negligible effect on the daily calculated ET. In conclusion, it seems that for off-line design purposes a simple linear dependence of ET on global radiation is sufficient. Adaptation of the model to existing facilities may require a more refined version of the VBR approach.

Coupled partial differential equations were developed to investigate which collector lengths are appropriate for drying and adsorbent regeneration under prevailing Ghanaian weather. Unlike approaches based on empirical data, the numerical model is more flexible. Effects of operational and design variables on outlet temperature and performance were systematically studied. Collector length and air speed affect performance indicators. Operational overall heat loss coefficient, an important characteristic of the collector, is not constant but varies during the day. With plausible physical parameters, the model describes the experimental data well. Collector lengths of 1.5 and 4.5 m suited drying and regeneration, respectively.

In the daily operation of a greenhouse, decisions must be made about the best deployment of equipment for generating heat and electricity. The purpose of this paper is twofold: (1)To demonstrate the feasibility and flexibility of an optimal control framework for allocating heat and electricity demand to available equipment, by application to two different configurations used in practice. (2)To show that for a given energy and electricity demand benefit can be obtained by minimizing costs during resource allocation. The allocation problem is formulated as an optimal control problem, with a pre-defined heat and electricity demand pattern as constraints. Two simplified, yet realistic, configurations are presented, one with a boiler and heat buffer, and a second one with an additional combined heat and power generator (CHP)and a second heat buffer. A direct comparison with the grower is possible on those days where the other equipment that was at the grower's disposal was not used (63 days in the available 2012 data set). On those days overall costs savings of 20% were obtained. This shows that a given heat demand does not come with a fixed price to pay. Rather, benefits can be obtained by determining the utilization of the equipment by dynamic optimization. It also appears that prior knowledge of gas and electricity prices in combination with dynamic optimization has a high potential for cost savings in horticultural practice. To determine the factors influencing the outcome, different sensitivities to the optimization result were analyzed.

Saving energy in greenhouses is an important issue for growers. Here, we present a method to minimize the total energy that is required to heat and cool a greenhouse. Using this method, the grower can define bounds for temperature, humidity, CO 2 concentration, and the maximum amount of CO 2 available. Given these settings, optimal control techniques can be used to minimize energy input. To do this, an existing greenhouse climate model for temperature and humidity was expanded to include a CO 2 balance. Heating, cooling, the amount of natural ventilation, and the injection of industrial CO 2 were used as control variables. Standard optimization settings were defined in order to compare the grower’s strategy with the optimal solution. This optimization resulted in a theoretical 47% reduction in heating, 15% reduction in cooling, and 10% reduction in CO 2 injection for the year 2012. The optimal control does not need to maintain a minimum pipe temperature, in contrast to current practice. When the minimum pipe temperature strategy of the grower was implemented, heating and CO 2 were reduced by 28% and 10% respectively. We also analyzed the effect of different bounds on optimal energy input. We found that as more freedom is given to the climate variables, the higher the potential energy savings. However, in practice the grower is in charge of defining the bounds. Thus, the potential energy savings critically depend on the choice of these bounds. This effect was analyzed by varying the bounds. However, because the effect can be demonstrated to the grower, the outcome has value to the grower with respect to decision making, an option that is not currently available in practice today.

Energy requirements for resource transport of algae cultivation are unknown. This work describes the quantitative analysis of energy requirements for water and CO2 transport. Algae cultivation models were combined with the quantitative logistic decision model 'BeWhere' for the regions Benelux (Northwest Europe), southern France and Sahara. For photobioreactors, the energy consumed for transport of water and CO2 turns out to be a small percentage of the energy contained in the algae biomass (0.1-3.6%). For raceway ponds the share for transport is higher (0.7-38.5%). The energy consumption for transport is the lowest in the Benelux due to good availability of both water and CO2. Analysing transport logistics is still important, despite the low energy consumption for transport. The results demonstrate that resource requirements, resource distribution and availability and transport networks have a profound effect on the location choices for algae cultivation.

The analyses of algae biorefinery performance are commonly based on fixed performance data for each processing step. In this work, we demonstrate a model-based combinatorial approach to derive the design-specific upstream energy consumption and biodiesel yield in the production of biodiesel from microalgae. Process models based on mass and energy balances and conversion relationships are presented for several possible process units in the algae processing train. They allow incorporating the effects of throughput capacity and process conditions, which is not possible in the data-based approach. Therefore, the effect of choices in the design on the overall performance can be quantified. The process models are organised in a superstructure to evaluate all combinations of routings. First, this is done for selected fixed design conditions, which is followed by optimisation of the process conditions for each route by maximising the net energy ratio (NER), based on upstream energy consumption and biodiesel yield. A scenario based on current energy production and state-of-the art techniques for algae processing is considered. The optimised process conditions yield NER values which are up to about 30% higher than those for fixed process conditions. In addition, the approach allows a quantitative bottleneck analysis for each process route. The model-based approach proves to be a versatile tool to guide the design of efficient microalgae processing systems.

In this work, the Flory Huggins Free Volume theory is used to interpret the sorption isotherms of broccoli from its composition and using physical properties of the components. This theory considers the mixing properties of water, biopolymers and solutes and has the potential to describe the sorption isotherms for varying product moisture content, composition and temperature. The required physical properties of the pure components in food became available in recent years and allow now the prediction of the sorption isotherms with this theory. Sorption isotherm experiments have been performed for broccoli florets and stalks, at two temperatures. Experimental data shows that the Flory Huggins Free Volume (FHFV) theory represents the sorption isotherm of fresh and blanched broccoli samples accurately. The results also show that blanching affects the sorption isotherm due to the change of composition via leaching solutes and the change of interaction parameter due to protein denaturation. © 2013 Springer Science+Business Media New York.

In cold-climate locations, where natural gas is burned during the day to enrich greenhouses with carbon dioxide, water tanks (buffers) are often used to store the surplus daytime heat for nighttime heating. A practical control strategy for filling (charging) and emptying (discharging) of the buffer, based on a virtual value (costate) of the stored heat (the state), is suggested and illustrated by simulation. Heating and ventilation decisions are obtained by maximizing, at each time step, the virtual increase in value of the greenhouse system (including stored heat). As long as the heat buffer is neither empty nor full, the virtual value (co-state) of the stored heat remains constant. When the buffer is full (towards the end of a day), this value is gradually decreased, until the buffer starts to discharge. When the buffer empties (towards the end of a night), the virtual value is gradually increased, until recharging of the buffer starts again. This heuristic strategy is meant to minimize the time that the buffer is empty or full, because in these states the buffer is inactive (ineffective). Simulations with an annual weather sequence show the following: (1) the winter-Time virtual value of stored heat is about equal to the actual cost of heat, while in summer it is close to zero; (2) the utilization of the buffer, judged by the time on the storage bounds (full or empty), is roughly uniform along the year; (3) the performance of the system improves asymptotically with an increase of the installed capacity of the buffer; (4) expensive energy (heat) results in reduced intensity of cultivation (less heat and less yield).

Microalgae productivity in tubular photobioreactors depends on algae species, location, tube diameter, biomass concentration, distance between tubes and for vertically stacked systems, the number of horizontal tubes per stack. A simulation model for horizontal and vertically stacked horizontal tubular reactors was made to quantify the effect of these decision variables on production yield. The model uses reactor dimensions, dynamic sunlight patterns over the day and year, and growth characteristics of algae species as inputs. Scenario studies were done to study the effect of decision variables on reactor performance in The Netherlands, France and Algeria. Results indicate that the areal biomass productivity in vertically stacked photobioreactors is 25–70% higher than in plain horizontal systems. Reactor design is location specific because light conditions differ. In The Netherlands, the best horizontal distance between tubes is 0.05 m for horizontal and 0.25 m for vertical systems. For France and Algeria, the best horizontal distance between vertical systems is 0.20 m and 0.15 m respectively. System performance can be improved further by using light reflection materials on the ground surface. Improving the transparency properties of tube material does not significantly affect areal productivity.

Polarization curves are of paramount importance for the detection of toxic components in microbial fuel cell (MFC) based biosensors. In this study, polarization curves were made under non-toxic conditions and under toxic conditions after the addition of various concentrations of nickel, bentazon, sodiumdodecyl sulfate and potassium ferricyanide. The experimental polarization curves show that toxic components have an effect on the electrochemically active bacteria in the cell. (Extended) Butler Volmer Monod (BVM) models were used to describe the polarization curves of the MFC under nontoxic and toxic conditions. It was possible to properly fit the (extended) BVM models using linear regression techniques to the polarization curves and to distinguish between different types of kinetic inhibitions. For each of the toxic components, the value of the kinetic inhibition constant Ki was also estimated from the experimental data. The value of Ki indicates the sensitivity of the sensor for a specific component and thus can be used for the selection of the biosensor for a toxic component. © 2012 by the authors.

Evaluation of the potential of algae production for biofuel and other products at various locations throughout the world requires assessment of algae productivity under varying light conditions and different reactor layouts. A model was developed to predict algae biomass production in flat panel photobioreactors using the interaction between light and algae growth for the algae species Phaeodactylum tricornutum and Thalassiosira pseudonana. The effect of location, variable sunlight and reactor layout on biomass production in single standing and parallel positioned flat panels was considered. Three latitudes were studied representing the Netherlands, France and Algeria. In single standing reactors the highest yearly biomass production is achieved in Algeria. During the year biomass production fluctuates the most in the Netherlands, while it is almost constant in Algeria. Several combinations of path lengths and biomass concentrations can result in the same optimal biomass production. The productivity in parallel place flat panels is strongly influenced by shading and diffuse light penetration between the panels. Panel orientation has a large effect on productivity and at higher latitudes the difference between north–south and east–west orientation may go up to 50%.

Conventionally, energy-saving techniques in drying technology are sequential in nature. First, the dryer is optimized without heat recovery and then, based on the obtained process conditions, heat recovery possibilities are explored. This work presents a methodology for energy-efficient adsorption dryer design that considers sensible and latent heat recovery as an integral part of drying system design. A one-step pinch-based optimization problem is formulated to determine the operating conditions for optimal energy performance of such an integrated system subject to product quality. Because the inlet and target stream properties of the heat recovery network are determined by the adsorption drying conditions, they are unknown a priori and thus are determined simultaneously within the overall optimization using the pinch location method. Energy balances are written above and below the various pinch point possibilities and the optimal pinch point is that which minimizes the amount of external heating utility required while satisfying drying and thermodynamic constraints. Results for a single-stage zeolite adsorption drying process with simultaneous heat recovery optimization show a 15% improvement in efficiency compared to a sequentially optimized system. The improvement is traceable to alterations in enthalpy-related variables like temperatures and flow rates. The discrepancy in optimal operating conditions between the sequential and simultaneous cases underscores the need to change system operating conditions when retrofitting for heat recovery because previous optimal conditions become suboptimal when heat recovery is introduced. Also, compared to a conventional dryer (without an adsorption process) operating under similar conditions, energy consumption is reduced by about 55%. © 2011 Taylor & Francis Group, LLC.

A new greenhouse type has been designed to study ways of decreasing water use by horticulture in semi-arid regions. To control the greenhouse a model-based control design is required. To this end a model is needed to predict the systems behaviour (1 day ahead), without much computational effort. A physics-based model is developed, based on enthalpy and mass balances. The (lumped) key parameters of the model are identified with a controlled random search algorithm. To increase estimation accuracy and reduce computation time, estimation in parts was applied, that is only a part of the whole model was used in combination with measured data for state values of neighbouring compartments. This results in parameter estimates that converge well. In order to keep the model information needs limited, the underlying process details were aggregated into a lumped parameter description, at the expense of time-varying parameters over the seasons. The parameter fluctuation over the year was studied by repeated monthly parameter estimations. Since parameters fluctuate significantly, further research will focus on the use of adaptive mechanisms to facilitate model-based control.

The operational range of a food production system can be used to obtain a variation in certain product characteristics. The range of product characteristics that can be simultaneously realized by an optimal choice of the process conditions is inherently limited. Knowledge of this feasible product range and the limitations therein would be of great help to the process/product innovator. In this article, the method of Pareto analysis, in combination with dynamic optimization, was introduced to map the feasibility range for multi-objective optimization problems. The Pareto front obtained for a set of conflicting objectives divides the product specification space into a feasible and a nonfeasible area. For process design, such an analysis is a versatile tool to evaluate different options and to search for extension of the operational range. In this work, such Pareto analysis was applied for bakery production. Bakery products might have conflicting specifications for the final quality (crispness, brownness and moisture content). The analysis shows how the feasible area was changed as the initial dough water content is modified. Furthermore, it was proven that by modification of the process (i.e., application of multiheating systems ¿ combined convective, microwave and radiation heating), the feasible area was extended significantly. Taking these options into account, the operational range of bakery systems was improved significantly.

Optimal operation strategies for greenhouse crop cultivation can be computed with open loop dynamic optimization. These solutions are obtained off-line, and are valid under nominal weather conditions only. On-line, feed-back control is needed to cope with deviations from the nominal weather. One of the issues in practical control is how to link the off-line nominal solution to on-line control. One option is to use a receding horizon controller with the same goal function as used off-line, but enhanced with a term based on the co-state of the slow crop states to encapsulate the long term goals. Loss measures are introduced to evaluate this solution against various other approximate solutions proposed in the literature. To our knowledge this is the first time that various sub-optimal solutions are clearly listed and analysed. A simplified, but transparent, example is used to illustrate the various losses. One of the results is that receding horizon optimal control with an adapted goal function is superior to other more common control solutions.

Zeolites have potential to increase efficiency of medium-temperature drying in the food industry. This work concerns the comparison between conventional dryers and dryers using air dehumidified by zeolite. Steady-state mass and energy balances have been used and the work concerns drying temperatures ranging from 52 to 70°C. Process integration based on pinch analysis has been applied and nine different heat exchanger networks for energy recovery are compared. Results indicated that dryers using air dehumidifier by zeolites are 10-18% more efficient than conventional dryers.

Measurement of the key process variables is essential during biopharmaceutical production. These measurements are often not available online. This work combines frequent online measurements with infrequent offline measurements to estimate the specific growth rate, biomass, and the oxygen mass transfer coefficient during continuous and fed-batch cultivations of Bordetella pertussis online using an Extended Kalman filter, parameter adaptation, and learning.

A new greenhouse type is designed to study ways of decreasing horticultural water use in semi-arid regions. To control the greenhouse a model based control design will be applied. Hereto a model is needed to predict the systems behavior (1 day ahead), without much computational effort. A physics-based model for this new type of greenhouse is developed, based on enthalpy and mass balances. The greenhouse is divided in four compartments; the plant area, the roof area, the heat exchanger and the soil. For all compartments only the main energy and mass fluxes are modeled, in order to keep the model simple. Since the model describes only the main characteristics of the system with physical equations, careful calibration and validation (systems identification) is needed. Real data gained from the experimental greenhouse are used in a controlled random search to find the optimal parameter values

A dynamic model was set up to describe the mineral content, fruit dry matter content, and biomass of greenhouse tomato, for use in an experiment aiming at controlling the fertigation so as to reach the best compromise between disinfection costs of the recycled water and income from fruit fresh weight production, while ensuring that the dry matter of the fruits is above a given quality standard. The model describes the effect of mineral shortage on growth, and has a simple mechanism to relate dry matter content to the total ion concentration in the substrate. Optimal control requires a quantitative model in state-space form. In order to reduce on-line computation time, the number of states was kept within reasonable limits by lumping leaves, fruits and substrate into three compartments. Therefore the model is labelled `big leaf, big fruit, big substrate¿ or 3Bigs model. Using parameters from literature and some calibration on previous experiments, quite acceptable fits were obtained for biomass, and dry matter and K, N, and Ca contents of the fruits. The model was subsequently used in a receding horizon optimal control experiment. Ion-concentrations in the drain as measured by ion-specific electrodes are fed back to correct the state of the model before the next values of the required drain flow and the fertilizer fluid aliquots are computed. Although it was not possible to operate the controller for more than six weeks, it could be observed that the controller behaviour was consistent with expectations in view of the given model and goal function. The fresh weight yield and the dry matter constraint dominate the behaviour. The results also suggest that currently used ion concentrations are higher than necessary for uninhibited growth.

Water resources are diminishing in many (semi) arid regions, thus becoming a concern in the (near) future. Desalination of brackish or salt water can be a good solution to provide water for agriculture and human consumption. The Watergy project proposes an integrated system in which plants and fresh water are produced. The system is closed and air is being cooled during the day with a central heat exchanger in a chimney. Since this heat exchanger is a vital part for the functioning of the system, it was decided to test its characteristics such as heat transfer and air velocities in a controlled environment. A chimney was built with a heat exchanger installed inside with a surface of 11 m2. Several experiments were conducted with varying layout of the heat exchanger, varying conditions and excitation signals. This paper describes the results of steady state situation with five baffles during heating and cooling. The measured air velocities are in the range of 0.72 0.6 m/s. The measured heat exchange coefficient is in the range of 25 28 W/m2K. The efficiencies of all the presented experiments are in the range of 70 ¿ 80%. The results of the experiments are compared to a simple physics based, steady state model that describes the convection, conduction and condensation processes. The model results are quite close to the experimental results; the maximum temperature deviation between model and observations on the water-side is 0.5¿C (at 40¿C) and on the air side 2.5°C. The accuracy of the model is sufficient to use it for design purposes and, later on, as a starting point for model based control of the greenhouse. Finally, the results of the experiments show that the heat transfer in the proposed design can be sufficient to cool and heat the Watergy greenhouse.

A (model-based) Product Quality Controller is presented for climate controlled operations involving agro-material, such as storage and transport. This controller belongs to the class of Model Predictive Controllers and fits in a previously developed hierarchical control structure. The new Product Quality Controller rejects disturbances and tracks the product quality by means of the product responses respiration and fermentation. To achieve an energy efficient operation the presented controller is closely linked with the (existing) local controllers. Local optimisation on the level of these local controllers allows (controlled) high-frequent climate fluctuations. This results in significant energy savings. The Product Quality Controller and the energy efficient local controllers are implemented in small-scale and full-scale industrial case studies on controlled atmosphere-container transport of apples. This yields direct control of product quality and a significant reduction in energy consumption.

A solar greenhouse has been designed that maximizes solar energy use and minimizes fossil energy consumption. It is based on a conventional Venlo greenhouse extended with a heat pump, a heat exchanger, an aquifer and ventilation with heat recovery. The goal is to minimize fossil energy consumption, while maximizing crop dry weight, keeping temperature and humidity within certain limits and satisfying a temperature integral. To achieve this the aim is to implement a receding horizon optimal controller. This type of controller computes optimal closed loop controls based on a cost function and a greenhouse and crop model. In this paper simulations of a simplified version of this closed loop optimal control system are presented. It is found that the boiler use is reduced, thereby reducing fossil energy use. Gas use is decreased by 77% compared to a conventional greenhouse with optimal control.

The development and market introduction of affordable ion specific sensors, like the ISFET sensor, has paved the way for completely new systems for application of fertilisers to crops in greenhouses. This paper describes the use of information feedback from flow sensors and ion specific sensors to regulate the supply of water and nutrients to a model gully. When a controller keeps the drain flow and the concentrations of the individual ions in the drain at a sufficiently high fixed value, the uptake of the plants is intrinsically compensated, i.e. the demand of the plants is exactly satisfied. Time series measurements of flow and concentration in supply and drain are used to design the controller for applying nutrients to a tomato crop in a near-practice greenhouse. Data are shown of fertiliser concentrations and flow in supply and drain during the design phase and test phase of the controller.

Progress in instrumentation and the need for quality control stimulate the rethinking of irrigation and fertilisation (fertigation) of crops in greenhouses. Feedback control using ion specific electrodes in the drain offers opportunities for automatic demand satisfaction. The fertigation system is characterised by pulse-wise water supply, which puts unconventional challenges to the measurement system. Limitations from practice require identification and control design procedures to be robust and simple. This paper introduces a reduced model for the system and describes the practical identification of the nutrient dynamics in a measurement gully on a time scale that is relevant to the crop.

The objective of the solar greenhouse project was the development of a Dutch greenhouse system for high value crop production without the use of fossil fuels. The project was completed and the results are reported here. The main approach was to first design a greenhouse system requiring much less energy, next to balance the availability of natural energy with the system's energy demand, and finally to design control algorithms for dynamic system control. This paper discusses the first two design steps. Increasing the insulation value of the greenhouse cover was the first step towards a reduction in energy demand. The challenge was in maintaining a high light transmission at the same time. A first generation of suitable materials was developed. The realizable energy saving is almost 40 %. The next reduction in fossil fuel requirement was accomplished by capturing solar energy from the greenhouse during the summer months, storing it in an underground aquifer at modest temperatures, and finally using the stored energy during the winter months by using heat pumps. Then the total realizable energy saving is more then 60%. For sustainable energy supply per ha greenhouse at this low energy demand 32 ha biomass is needed, or 600 kW nominal wind power or 1.2 ha PV assuming storage via the public grid.

Explorative experiments were done to investigate the possibility that tomato plants infected by Botrytis cinerea have a different emission of volatile organic compounds (VOC) than healthy plants. This was done by headspace analysis of volatiles emitted by detached leaves of infected and healthy plants. Principal component analysis (PCA) of GC-FID chromatograms revealed clearly separated clusters between infected and control leaves and identification of an interesting compound. In further analysis by GC-MS, the significantly distinctive component (p≤0.05) was identified as the sesquiterpene α-copaene. In previous work on herbivore damage, α-copaene was not distinctive, which may suggest that α-copaene may be specifically associated to fungal infections in tomato.

To enhance development and acceptance of new control strategies, a standard simulation benchmarking methodology to evaluate the performance of wastewater treatment plants has recently been proposed. The proposed methodology is, however, for a typical plant and that works under typical loading and environmental conditions. Thus, benchmarking a full-scale plant working under different situations is still a problem that needs to be solved. This paper proposes a data based approach to benchmark any specific full-scale activated sludge plant used for carbon and nitrogen removal and using real design, operational and performance data. The advantage of the proposed approach over the "one-typical-plant" approach, is that it takes into account the very specific design and operational characteristics of the plant that will be benchmarked. A real-world illustrative example is also presented in this paperTo enhance development and acceptance of new control strategies, a standard simulation benchmarking methodology to evaluate the performance of wastewater treatment plants has recently been proposed. The proposed methodology is, however, for a typical plant and that works under typical loading and environmental conditions. Thus, benchmarking a full-scale plant working under different situations is still a problem that needs to be solved. This paper proposes a data based approach to benchmark any specific full-scale activated sludge plant used for carbon and nitrogen removal and using real design, operational and performance data. The advantage of the proposed approach over the "one-typical-plant" approach, is that it takes into account the very specific design and operational characteristics of the plant that will be benchmarked. A real-world illustrative example is also presented in this paper. (C) 2003 Elsevier Science Ltd. All rights reserved.

Unexpectedly, primary concern of bio-pharmaceutical industry is not optimisation of product yield or cost reduction, but consistency in production and product quality. This paper describes the methodology and experimental results of specific growth-rate control for vaccine production. The controller is based on four modules: specific growth rate observer, biomass observer, feed calculation, and feedback controller. Peculiarities are: the algorithm is based on oxygen uptake rate only, the organism is dual-substrate limited and requires multiple feed rates, the algorithm proved to work well in labscale experiments, consistency is improved, and production efficiency increased up to 4 times.

Since high concentration levels of nitrate in lettuce and other leafy vegetables are undesirable, cultivation of lettuce according to specified governmental regulations is currently an important issue. Therefore, methods are sought in order to produce a lettuce crop that allow maximization of the profits of the grower while at the same time insuring the quality of the crops. Using a two-state dynamic lettuce model that predicts the amount of nitrate at harvest time, an optimal control problem with terminal constraints is formulated. The situation considered may be relevant in a plant factory where a fixed head weight should be reached in fixed time while minimizing light input. First, optimal trajectories of light, CO2 and temperature are calculated using the adjustable control weight (ACW) gradient method. Subsequently, novel, efficient and modified differential evolution (DE) algorithms are used to obtain an approximate solution to the same optimal control problem. While the gradient method yields a more accurate result, the optimum may be local. In order to exploit the salient characteristics of a DE algorithm as a global direct search method, a hybrid-combined approach is proposed. An approximate solution obtained with a DE algorithm is used to initialize the ACW gradient method. Although local minima did not seem to occur in this particular case, the results show the feasibility of this approach. # 2003 Published by Elsevier Science B.V.

In a modern greenhouse there are a number of alternative systems that can be deployed to control the climate, and the choice what to use and when is not easy for the grower. A novel management system is proposed, consisting of an energy input minimizing module, and a module to realise the determined input with the available equipment. The current paper describes the energy minimization part. A dynamic optimization tool based on optimal control theory was used to obtain time trajectories of the energy flux that minimizes total external energy input over the year, while maintaining greenhouse air temperature and humidity between grower defined bounds. By giving the grower the lead in defining the bounds, the method stays as closely as possible to the grower’s daily practice and experience, and no crop production models and market prices are needed. The underlying dynamic model of temperature and humidity, based on known physical principles and parameters, compared very well with unique, year round high frequent data from a commercial rose greenhouse. A relatively simple crop transpiration model was validated separately, with very good results. It was shown that over twelve selected days, distributed over the entire year, the energy saving potential as compared to the actual grower’s practice is substantial. This potential was related to the definition of lower and upper bounds, less natural ventilation at colder days, and more natural ventilation and less heating at warmer days. The prominent role of the bounds was clearly demonstrated. Relaxing the temperature and humidity bounds decreases the energy input to the greenhouse. While this is obvious, the quantification of the effect as demonstrated here is of great interest to growers, and is essential for the development of the second part of the system.

While a tomato crop grows on the time-scale of weeks, the greenhouse climate changes on a time-scale of minutes. The economic optimal control problem of producing good quality crops against minimum input of resources is tackled by a two time-scale decomposition. First, the sub-problem associated to the slow crop evolution is solved off-line, leading to a seasonal pattern for the co-states of the amount of assimilates produced by photosynthesis, and the fruit and leaf weights. These co-states can be interpreted as the marginal prices of a unit of assimilate, leaf and fruit. Next, they are used in the goal function of an on-line receding horizon control (RHOC) of the greenhouse climate, thus balancing costs of heating and CO2-dosage against predicted benefits from harvesting, while profiting as much as possible from the available solar radiation. Simulations using the time-varying co-states are compared to experimental results obtained with fixed co-states. It appears that the on-line control is sensitive to the time evolution of the co-states, suggesting that it is advantageous to repeat the seasonal optimisation from time to time to adjust the co-states to the past weather and realised crop state. ? 2002 Elsevier Science Ltd. All rights reserved.

Control of nitrate content and other growth characteristics of leafy vegetables such as greenhouse lettuce is an increasingly topical subject in a European context. This preliminary study focuses on the control of characteristics in greenhouse lettuce at harvest time on the basis of dynamic optimization techniques in which a novel lettuce growth model of the plant is embedded. In order to gain insight in the growth model itself first various constant input responses are presented, followed by some results of a more challenging problem in which dynamic input trajectories are assumed. The 'step-by-step' approach starts with a concise model definition and progresses via the constant input response and the definition of an appropriate cost (defining optimality) towards a dynamic optimal solution. The method leads to some interesting design considerations and suggestions for further research, which are made in a concluding section.

A study was made of the robustness of controllers for fluidized bed tea drying. Several controller configurations have been designed and studied in previous work. Tuning of these controllers is possible using a transfer function estimated from the frequency behaviour of a validated simulation model under normal operating conditions. In this work a range of operating conditions deviating from the standard conditions was studied. Controller tuning determined by the Cohen & Coon or Ziegler–Nichols methods was not found to be robust over the range of conditions tested. A different method was developed, based on dryer modelling, to establish a range of controller settings giving minimal Integral Squared Error while maintaining adequate gain and phase margins. These settings were found to be suitable for the whole range of conditions tested. A simplification to the inferential controller, using gains only rather than complete transfer functions in the inferential estimator, was shown to be justified.

Open loop optimal control strategies for the thermal sterilization of canned foods are computed and analysed. The optimal retort temperature profiles ensure that the required degree of sterilization is met whilst minimising costs. The costs are defined in terms of batch time and retention of nutrient value in the food material. The model is a finite dimensional approximation of the infinite dimensional system. The errors due to the finite dimensional approximation and the uncertainty in the thermal diffusivity parameter of the model are considered. Based on the analysis a closed loop receding horizon optimal controller is designed. Simulation experiments reveal the robustness of the receding horizon optimal controller with respect to uncertainty in the thermal diffusivity parameter. The simulations also reveal that the loss of performance is relatively small and that the batch-time may be reduced significantly.

An evolutionary program, based on a real-code genetic algorithm (GA), is applied to calculate optimal control policies for bioreactors. The GA is used as a nonlinear optimizer in combination with simulation software and constraint handling procedures. A new class of GA-operators is introduced to obtain smooth control trajectories, which leads also to a drastic reduction in computational load. The proposed method is easy to understand and has no restrictions on the model type and structure. The performance and optimal trajectories obtained by the extended GA are compared with those calculated with two common methods: (i) dynamic programming, and (ii) a Hamiltonian based gradient algorithm. The GA proved to be a good and often superior alternative for solving optimal control problems.

Limiting the supply of nitrate to winter-lettuce in greenhouses is one approach to assuring an acceptably low nitrate level in the marketed produce. To optimize growth under such conditions, a two-state-variable lettuce model, originally developed to predict the nitrate concentration when the supply of nutrients is unlimited (Seginer et al., 1998), has been modified to take account of growth limited by nitrate supply. Nitrate in the plant serves two functions: it is a vital raw material for protein synthesis and also an important osmoticum in the cell sap. The plant model responds to limited nitrate supply by placing a higher priority on its role as raw material. Nitrate in the cell sap is replaced, as much as needed, by assimilates (sugar), freeing the recovered nitrate for protein synthesis. The modified model includes a balance equation for nitrate in the plant dSNv/dt = FNrv - rFCvs , where SNv is nitrate-N content, FNrv is nitrate-N uptake by the plant, FCvs is the rate of structural growth and r is the nitrogen content of the structural material. dSNv/dt is determined by the rate of change of the assimilate content of the cell sap, as described by the original model. If nitrate supply is abundant, its rate of uptake, namely FNrv, is determined as a remainder. If nitrate supply is limiting, and therefore is all taken up by the plant, it is the nitrate used for growth, rFCvs, that is determined by the balance. Comparison with limited experimental results produced a good agreement.

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.

Soluble carbohydrates and nitrate in the cell sap of leafy vegetables act as complementary osmotic solutes to maintain the plant's turgor pressure. The implication is that nitrate at harvest time can be kept low by manipulating the source-sink balance. Pseudo-static calculations based on a model developed earlier fairly well reproduced the seasonal variability of nitrate levels in crops grown and harvested at near-monthly intervals. This paper concentrates on the dynamic behaviour of this model by calibrating it to available data of lettuce growth, and by performing a sensitivity analysis The model contains inhibition functions which are activated when the internal assimilate carbon concentration is near an upper or lower limit, in order to restrict photosynthesis or growth, respectively. Sensitivity analysis and calibration contour plots reveal that estimates of the growth parameters have a large uncertainty when growth is limited by carbon availability. In regions that are of interest for nitrate control, i.e. in the sink-limited case, all parameters determining the source-sink balance are important. Consequently, experiments must be designed such that they cover sink-limited conditions.

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 system's 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 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.

This work concerns the combined optimization of the retention of bioactive components and energy efficiency during drying of broccoli. Kinetics for the degradation of glucosinolates, vitamin C and drying of broccoli are used to calculate optimal drying trajectories for the control variables air flow rate and temperature. It is shown from plots of the optimal drying trajectories in moisture–temperature state diagrams with degradation and drying rates, that areas with high degradation rates are circumvented. The optimized drying strategies result in significant improvement of energy efficiency (65%) and vitamin C retention of 55%.

Through simulation, the optimal control of tomato crop production in a greenhouse based on an economic criterion, is compared with the result obtained with a conventional classical controller. The result is 6 % increase in profit and 43 % increase in energy efficiency over a characteristic autumn day. The solution of the optimal control problem is not at all straightforward. The greenhouse-tomato system is stiff. Singular perturbation theory is usually applied to cope with stiffness in optimal control problems, but in this case direct application is problematic due to the rapid external disturbances. In this paper the problem is solved by adapting a method proposed earlier by Van Henten (1994).

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.