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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.

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.

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 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.

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.

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

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.

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.

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.

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.

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.

This work establishes a relationship between dryer energy performance and controllability using energy balances and process resiliency analysis. It is shown that using the process gain matrix, the dryer energy efficiency can be reliably calculated with conditions for simultaneous controllability improvement established. By incorporating a drying rate modifying system such as a desiccant dehumidifier as an add-on, these conditions are shown to be achievable due to the extra dehumidification which can be manipulated using the additional degrees of freedom introduced by the sorption system. Due to the adsorbent regulation properties which are enhanced by high-temperature regeneration, the resilience of energy performance to disturbances is significantly improved compared to conventional dryers. Also, a desiccant system performance indicator, the "adsorber-regenerator net energy efficiency ARNEE" is introduced and it is shown that energy efficiency improvement is possible only if the ARNEE is greater than the energy efficiency of the stand-alone dryer. © 2013 Elsevier Ltd.

This work analyzes the synergy between two complementary unit operations - adsorbent dehumidification and drying - and presents a mixed integer nonlinear programming approach to optimize energy performance in a two-stage system. Combined with active constraint analysis, the adsorbent properties that promote energy performance are derived. Microporous adsorbents with higher sorption capacities at low vapor pressures and requiring higher regeneration temperatures are preferred for ambient air dehumidification in the first stage. For exhaust air dehumidification, mesoporous adsorbents with lower regeneration temperatures are preferred such that the exhaust air from the first regeneration stage can sufficiently regenerate them. For drying below 50 C, energy consumption reductions of about 70% are achieved compared to conventional dryers without adsorbent dehumidification depending on adsorbent properties. The results demonstrate the usefulness of superstructure optimization in matching the drying process with the capabilities of the adsorbents to enhance process synergy for improved energy efficiency. © 2013 American Chemical Society.

This work presents an approach to compute dryer energy efficiency using air flowrate step responses and establish a link between drying energy efficiency and process controllability. The approach is based on the temperature drop between the dryer inlet and outlet air under adiabatic conditions and so decouples water evaporation from heat loss and product heating effects on dryer temperature drop. As such, the computation is accurate even for dryers with significant heat losses where the traditional use of actual temperature drop measurements is grossly inaccurate. The approach is tested and verified on two experimental case studies involving significant heat losses: the first, a continuous fluidized-bed dryer (from literature); the second, a conventional and zeolite wheel-assisted batch dryer designed in the current study. © 2013 Copyright Taylor and Francis Group, LLC.

This work shows from energy balances and process resiliency analysis, the relationship between dryer controllability and energy performance. It is shown that using the process gain matrix, the dryer temperature drop and hence, energy efficiency can be reliably calculated. Conditions necessary for simultaneous energy efficiency and controllability improvement are established. By pre-conditioning the drying air using a desiccant adsorption system, such conditions are shown to be achievable for the same input choice as a conventional dryer. Also, extra degrees of freedom introduced by the desiccant system promote controllability with the advantage best exploited using full multivariable control. Energy efficiency disturbance resilience is also improved. © 2012 Elsevier B.V.

This work presents a mixed integer nonlinear programming (MINLP) formulation for the design of energy-efficient multistage adsorption dryers within constraints on product temperature and moisture content. Apart from optimizing temperatures and flows, the aim is to select the most efficient adsorbent per stage and product to air flow configuration. Superstructure models consisting of commonly used adsorbents such as zeolite, alumina, and silica-gel are developed and optimized for a two-stage, low-temperature, adsorption drying system. Results show that the optimal configuration is a hybrid system with zeolite as the first-stage adsorbent and silica-gel as the second-stage adsorbent in counter-current flow between drying air and product. A specific energy consumption of 2,275 kJ/kg is achieved, which reduces to 1,730 kJ/kg with heat recovery by a heat exchanger. Compared to a conventional two-stage dryer at the same drying temperature, this represents a 59% reduction in energy consumption. The optimal system ensures the exhaust air temperature of the first-stage regenerator is high enough to regenerate the second-stage adsorbent so no utility energy is spent in the second stage. A higher second-stage adsorbent wheel speed favors energy performance as it becomes optimized for energy recovery while the first is optimized for dehumidification. Although this work considers three candidate adsorbents in a two-stage system, the same reasoning can be applied to systems with more stages and adsorbents. The developed superstructure optimization methodology can, by extension, be applied to optimize multistage hybrid drying systems in general for any objective. © 2012 Copyright Taylor and Francis Group, LLC.

This work presents a mixed integer nonlinear programming method for the design of energy efficient multistage adsorption dryers within product temperature and moisture constraints. The aim is to find the adsorbent type and properties that minimize specific energy consumption. The results show that the adsorbents chosen in each stage have highest sorption capacities for corresponding drying air conditions. Heat requirements are matched so, zero utility energy is spent regenerating the second stage adsorbent. The adsorbent flow speeds are such that the first stage is optimized for dehumidification and the second for heat recovery. Overall, the optimal system reduces specific energy consumption by about 64% compared to a conventional system at the same drying temperature. Also, drying capacity is improved which permits the use of smaller dryers. © 2012 Elsevier B.V.

The cultivation of algae in photo-bioreactors shows similarities to crop cultivation in greenhouses, especially when the reactors are driven by sun light. Advanced methodologies for dynamic optimization and optimal control for greenhouses are known from earlier research. The aim here is to extend these methodologies to microalgae cultivated in a flat plate photo-bioreactor. A one-state space model for the algal biomass in the reactor is presented. The growth rate vs. light curve is parameterized on the basis of experimental evidence. Spatial distribution of light and growth rate between the plates is also considered. The control variable is the dilution rate. Dynamic optimal control trajectories are presented for various choices of goal function and external solar irradiation trajectories over a horizon of 3 days. It was found that the algae present in the reactor at final time represent a value for the future. Numerical and theoretical results suggest that the control is bang-(singular-)bang, with a strong dependence on the weather. The optimal biomass also depends on the available light, and achieving it to reach a new optimal steady cycle after a prolonged change in weather may take several days. A preliminary theoretical analysis suggests a control law that maximizes the effective growth rate. The analysis shows that like in the greenhouse case, the co-state of the algal biomass plays a pivot role in developing on-line controllers.

The systematic design of an autonomous platform for robotic weeding research in arable farming is described. The long term objective of the project is the replacement of hand weeding in organic farming by a device working autonomously at field level. The distinguishing feature of the described design procedure is the use of a structured design approach, which forces the designer to systematically review and compare alternative solution options, thus preventing the selection of solutions based on prejudice or belief. The result of the design is a versatile research vehicle with a diesel engine, hydraulic transmission, four-wheel drive and four-wheel steering. The robustness of the vehicle and the open software architecture permit the investigation of a wide spectrum of research options for intra-row weed detection and weeding actuators.

The shear stresses in printed scaffold systems for tissue engineering depend on the flow properties and void volume in the scaffold. In this work, computational fluid dynamics (CFD) is used to simulate flow fields within porous scaffolds used for cell growth. From these models the shear stresses acting on the scaffold fibres are calculated. The results led to the conclusion that the Darcian (k 1) permeability constant is a good predictor for the shear stresses in scaffold systems for tissue engineering. This permeability constant is easy to calculate from the distance between and thickness of the fibres used in a 3D printed scaffold. As a consequence computational effort and specialists for CFD can be circumvented by using this permeability constant to predict the shear stresses. If the permeability constant is below a critical value, cell growth within the specific scaffold design may cause a significant increase in shear stress. Such a design should therefore be avoided when the shear stress experienced by the cells should remain in the same order of magnitude.

Application of advanced controllers in horticultural practice requires detailed models. Even highly sophisticated models require regular attention from the user due to changing circumstances like plant growth, changing material properties and modifications in greenhouse design and layout. Moreover, their calibration is data demanding and laborious. This study explores the suitability of the extended Kalman filter (EKF) for automatic, on-line estimation and adaptation of parameters in a physics-based greenhouse model. The method was tested with measured data recorded over a period of 1 year, and with a model that describes the air temperature and moisture content in the Watergy greenhouse. In order to keep the parameters estimation problem tractable, and to improve the local accuracy of the parameters, separate EKFs are applied to sub-systems, using observation data at the sub-system boundaries. The filter adequately adjusts parameter values, thus significantly improving the model fit as compared to simulations with no-varying parameters. It appears that the filter is robust with respect to sudden changes in the system; when a disturbance occurs, such as pruning of plants or emergency opening of the windows, the EKF changes the parameter values accordingly. The result suggests that the extended Kalman filter is, indeed, a suitable method to provide the required automatic adaptation to time-varying phenomena when modeling is impractical.

Many papers describe applications of advanced controllers in greenhouses. As control literature focusses on control algorithms, the layout of the measurement and control system is usually underexposed. Unfortunately, commercially available greenhouse climate control systems do not have the necessary flexibility to accommodate these types of controllers. This paper describes a systematic approach to the design of a flexible measurement and control system, that can be adjusted to suit most control research in horticulture. Individual functions within a measurement and control system are identified and alternative solutions are given. The approach is applied to the design of the measurement and control system for the Watergy greenhouse. The controllers (in software) are versatile and setpoints can be generated by the easy-to-use user interface as well as by external software (like Matlab). Data is centrally stored and the system is easy to expand. It has been shown that the system has functioned robustly for the last 3 years. It has the capability to serve as basis for research and testing of various advanced control strategies in the Watergy system with adaptive model-based control as a final goal.

Measurement of the key process variables is essential during biopharmaceutical production. These measurements are often not available online. This work combines frequent online measurements (oxygen uptake rate) with infrequent offline measurements (biomass) to estimate the specific growth rate, biomass, and the oxygen transfer coefficient (kLa) online. The system consists of an extended Kalman filter and parameter adaptation for the time-varying kLa. Tuning is based on minimization of the error between the simulation and the estimation. Although the process itself is not stable, stability of the observer is evaluated heuristically by application of the Routh criterion. Performance and convergence of the observer are shown in both simulations and experiments in continuous and fed-batch cultivations of Bordetella pertussis.

The available time span for food product and process innovation is steadily decreasing, and to increase the efficacy of the development cycles, systematic design procedures can be used to develop new and to redesign existing processes. The Conceptual Process Design (CPD) methodologies used in chemical industry might also be applied in the food industry to rethink their systems and to break down the complexity of problems into several hierarchical levels. The Delft design matrix, a combination of the iterative design procedure, hierarchical decomposition and task driven methods, is a promising tool which can cover the different objectives and criteria of food design. This article analyzes the potential applicability of Delft Design Matrix as a CPD methodology for food process design and illustrates its functionality with the design of a bakery production system.

Quality driven design uses specified product qualities as a starting point for process design. By backward reasoning the required process conditions and processing system were found. In this work dynamic optimization was used as a tool to generate processing solutions for baking processes by calculating optimal operation strategies which give a basis for process and unit operation design. Two different approaches for dynamic optimization had been applied: calculation of continuous trajectories based on the calculus of variations (1) and calculation of switching trajectories by using control vector parameterization (2). Optimization of bakery processes was performed for different product specifications and by using different heating sources: convective, microwave and radiation heating. Moreover, effects of variations in dough properties on the optimal processing system were also evaluated. The results showed that dynamic optimization procedures were versatile tools to achieve a better and a more flexible design by generating a number of solutions from the specified final product qualities. Furthermore, the results underpinned the well known empirical fact that different final product specifications require different baking strategies. It was also shown that the initial dough properties have significant effect on baking procedures, and that combining several heating inputs improved the flexibility of the process operation. In addition, optimization for continuous trajectories gave overall a better result than using the switching trajectories.

Two-dimensional computational fluid dynamics calculations for multistage zeolite drying are performed for two dryer configurations (1) a continuous moving bed zeolite dryer and (2) a discrete bed zeolite dryer. The calculations concern drying of tarragon (Artemisia dracunculus L.) as an herbal product. The results reveal the profiles of water, vapor, and temperature in dryer, adsorber, and regenerator in the flow directions. The thermal efficiency ranges between 80 and 90% and is close to overall model calculations. The performance of continuous moving bed zeolite dryer is the best. Residence time of air, product, and zeolite are in accordance to other drying systems.

Model reduction, linearisation and discretisation are standard techniques in systems theory to simplify nonlinear models. In this paper, these techniques are used on a nonlinear model of a storage facility with forced ventilation with ambient air. Consequently, weather forecasts are needed to predict the systems’ trajectory. Because weather forecasts inherit uncertainty, the storage model is amenable to uncertainty. Based on a linearised version of the storage model, standard error propagation rules have been used to predict the system uncertainty analytically. Optimal control calculates controls in such a way that a prespecified cost criterion is minimised. As the uncertainty of the storage system is subject to ventilation with outside air, uncertainty of the model state increases with increased ventilation. The model uncertainty is integrated into the cost criterion to allow a trade-off between an optimal nominal solution and a minimum variance control solution. In this respect, the predicted 95% confidence limits are kept as close as possible to the reference trajectory.

In bakery production, product quality attributes as crispness, brownness, crumb and water content are developed by the transformations that occur during baking and which are initiated by heating. A quality driven procedure requires process optimization to improve bakery production and to find operational procedures for new products. Control vector parameterization (CVP) is an effective method for the optimization procedure. However, for accurate optimization with a large number of parameters CVP optimization takes a long computation time. In this work, an improved method for direct dynamic optimization using CVP is presented. The method uses a sensitivity based step size refinement for the selection of control input parameters. The optimization starts with a coarse discretization level for the control input in time. In successive iterations the step size was refined for the parameters for which the performance index has a sensitivity value above a threshold value. With this selection, optimization is continued for a selected group of input parameters while the other nonsensitive parameters (below threshold) are kept constant. Increasing the threshold value lowers the computation time, however the obtained performance index becomes less. A threshold value in the range of 10–20% of the mean sensitivity satisfies well. The method gives a better solution for a lower computation effort than single run optimization with a large number of parameters or refinement procedures without selection.

The development of food production processes is facilitated by tools which explore the interaction between process design, operation conditions and product characteristics. In this work an approach how to set-up a simulation model is presented for the phenomena and transformations which occur during baking and which fix the product quality. The simulation model has three consecutive parts: mass and heat transport in the product, transformations concerning starch state transition and color, and the formation of quality attributes (color, softness, crispness and staling). The model for mass and heat transfer is based on laws of conservation and expressed in partial differential equations for spatial products. The starch state transition and color formation are a mixture of qualitative and quantitative information, while the product quality model is mainly based on qualitative information. The model is applied to three bakery products: bread, biscuit and a cake-type. The results show that the model estimates the product quality and its transformations as a function of dough composition, baking and storage condition. The results fit well to observed changes of properties and product quality during baking. Industrial relevance Food industries require tools to evaluate processing options in the feasibility phase of process design. Therefore, simulation of process models is important for this purpose. However, knowledge of different aspects is subject to area of expertise (for example heat and mass transfer versus product quality formation) and often these areas are hardly connected. This work presents a systematic modeling approach for the dominant processes during baking and their interconnection. The main functions of the model are to explore the consequences of choices in design, to rank design options and to find in what direction properties will change when operational conditions change. Moreover, the model can be used for sensitivity analysis to explore on what items further information must be gathered.

This work discusses the potential of three multistage zeolite drying systems (counter-, co-, and cross-current) with a varying number of stages. The evaluation showed that for 2¿4 stages with heat recovery the efficiency of the systems ranges between 80 and 90%. Additionally, by introducing a compressor, the latent heat in the exhaust air from the regenerator is recovered and used to heat the inlet air for an additional drying stage. As a result, for the counter-current drying system and compressor pressure 1.5¿2 bar, a maximum energy efficiency of 120% is achieved, which results in halving the energy consumption compared to conventional drying systems.

A wide range of various classical and modern control methods is classified from the point of view of required information and put in the context of optimization.