Model Predictive Temperature Control Research Articles

Model predictive temperature control of a closed-loop spray cooling system

A dynamic model of a closed-loop spray cooling system was proposed, and a model predictive temperature control algorithm was presented. The presented algorithm could quantitively estimate sudden changes in heat load, and then plan an optimized control path along which the temperature trajectory was numerically predicted to be the most stable. Then, the presented algorithm was integrated into a closed-loop spray cooling system, and its control performance was numerically studied. The results showed that, following a 20 W downward step change in heat load, the temperature was re-stabilized in 4 s, with its oscillation less than 0.14 K. Under identical conditions, the presented algorithm outperformed PID in terms of temperature stability, with its peak time shorter by 25–55%, settling time shorter by 38–51%, and average temperature offset smaller by 50–60%. This advantage was mainly because the presented algorithm compensated for hydraulic and thermal lags, two sources of control nonlinearity, by excessive regulation in advance. Unexpectedly, temperature overshoots of the presented algorithm and PID did not differ significantly, seemingly due to the algorithm-inherent estimation lag. From a system design perspective, the length of pump pipeline should be as short as possible, which proved to help reduce overshoot and settling time. Similarly, addition in heat load’s mass also helped reduce overshoot, but would result in an unfavorable long settling time, and vice versa. The simulation results were validated by experimental data, with error less than 10%.

Extended state observer-based model predictive temperature control of mechanically pumped two-phase loop: An experimental study

Mechanically pumped two-phase loop (MPTL), as an emerging two-phase heat dissipation transformational technique, has made booming progress in some critical but tough cases such as space stations and high-performance chips. However, accurate temperature regulation of MPTL is intractable due to periodic heat load disturbance, measurement noise, and micro-channel boiling nonlinearity. To address these issues, an extended state observer-based model predictive control (ESOMPC) strategy is developed in this paper. To describe the process model, this paper builds an MPTL platform equipped with a micro-channel evaporator, based on which a series of sinusoidal heat load variation is carried out to show the effects on the wall temperature. Open-loop step experiments are then conducted in terms of flow rate of the working fluid so that the process model can be identified and the nonlinearity can be analyzed. Then, the ESOMPC is developed by incorporating the disturbance information in the optimization framework of the MPC. The ESO part is utilized to estimate the unknown disturbances and compensate the nonlinearity of MPTL, and thus enhance the disturbance mitigation property of the MPC algorithm. Finally, the closed-loop experiments verifies the efficacy of the proposed solution, showing smaller temperature fluctuation and shorter settling time than the conventional controller. The results indicate the great potential of the proposed ESOMPC in enhancing the temperature control accuracy of MPTL in terms of thermal management performance.

Feed-forward offset-free model predictive temperature control for proton exchange membrane fuel cell: An experimental study

Safe and economic operation of an open-cathode proton exchange membrane fuel cell (PEMFC) requires an efficient thermal management strategy. The stack temperature regulation in PEMFC is, however, challenging due to often stringent set-point tracking tasks, frequent load fluctuations, constrained manipulated variables, and various modeling uncertainties and nonlinearities. To this end, a feed-forward offset-free model predictive control (MPC) approach, aiming at uncertainties resolving and disturbance mitigation, is developed to simultaneously address the above difficulties. In the proposed framework, the information about the measured power load fluctuations is used in the optimization algorithm as feed-forward information to eventually mitigate the influence of load fluctuations on the controlled output and increases the overall control quality. Additionally, the unmodeled dynamics and the other unmeasurable disturbances/uncertainties are collectively considered as an extended state of the system (to achieve zero static errors) and the on-line reconstructed aggregated disturbances is continuously sent to the MPC algorithm to increase its optimization performance and to achieve offset-free control objectives. The obtained results are quantitatively compared with conventional control strategies for PEMFCs, including a model-based PI controller, its modification utilizing disturbance feed-forward, and a standard offset-free MPC (i.e. without feed-forward). Both the simulations, realized in MATLAB/Simulink, and hardware experiments, conducted on a 500 W PEMFC testbed, show excellence of the proposed feed-forward offset-free MPC consisting in faster temperature tracking and higher robustness. The obtained satisfactory results show the introduced control solution to be a promising prospect and help accelerating further applications of PEMFCs.

Demand Side Electric Energy Consumption Optimization in a Smart Household Using Scheduling and Model Predictive Temperature Control

Abstract Utility companies seek to increase energy efficiency and productivity and try to reduce peak loads. This often involves consumer-side demand management in residential areas using dynamic time-of-use (ToU) tariff. Such strategies work if the consumer-side response is at least partly automated using some real-time optimization strategy. Our paper proposes a consumer-side optimization and control framework for scheduling the electric appliances in a smart household and preserving a thermal comfort level through an electric heating system. Our framework consists of two optimization components interacting with each other. The first optimization component schedules the home appliances based on a mixed integer programming approach. An electric vehicle (EV) is considered as a special home appliance with an energy storage capability. The second optimization component is the model predictive control (MPC) strategy for the electric heating system, such that the input constraints are defined by the scheduling results of the first component. Due to outside temperature variations, the input constraints may impede the MPC to maintain the required thermal comfort, which triggers a rescheduling event for the first component. The efficiency of the framework is presented in multiple simulations for scenarios with different consumer behaviors.

Model predictive temperature control and ageing estimation for an insulated cool box

The transport of perishable goods in insulated cool boxes (ICB) is affected by strong disturbances and requires an effective temperature control scheme. Model predictive control (MPC) can provide such a performing control scheme, however, the strong disturbance of irradiation by the sun can be rarely measured. Furthermore, the ageing of the ICB’s insulation is a parameter of vital interest for logistic and economic planning although it cannot be directly measured. In this work a MPC and a custom disturbance observer is presented, which provide an estimate of the current irradiation and the ageing state, respectively. This is obtained by first estimating the excess heat loss of the ICB, and then splitting up this heat loss into an irradiation part and a part due to ageing of the ICB’s insulation. The ageing model is assumed to be parametric, and a nonlinear estimation scheme is employed for parameter tuning. A suitable MPC formulation guarantees offset-free tracking of the desired temperature. The performance of the overall concept is demonstrated in a simulation for the nominal case, for increasing measurement noise, and for an abrupt change in the ageing parameters. Additionally, a non-parametric estimate for the confidence interval of the end-of-life prediction of the ICB is given.

Realization of online optimizing control in an industrial semi-batch polymerization

In this work, the realization of an online optimizing control scheme for an industrial semi-batch polymerization reactor is discussed in detail. The goal of the work is the automatic minimization of the duration of the batch without violating the tight constraints for the product specification which translate into stringent temperature control requirements for a highly exothermic reaction. Crucial factors for a successful industrial implementation of the control scheme are the development and the validation of a process model that is suitable for process optimization purposes and the estimation of unmeasured process states and the online compensation of model uncertainties. Two implementations are proposed, a direct online optimizing control scheme and a simplified scheme that combines a model-predictive temperature controller and a monomer feed controller that steers the cooling power to a predefined value in a cascaded fashion. We show by simulation results with a validated process model that both schemes achieve the goals of tight temperature control and reduction of the batch time. The performance of the NMPC controller is superior, on the other hand the cascaded scheme could be directly implemented into the DCS of the plant and is in daily operation while the online optimizing scheme requires an additional computer and is currently in the test phase.

Model predictive temperature control in long ducts by means of a neural network approximation tool

In this paper, a nonlinear model predictive control (MPC) configuration for hyperbolic distributed thermal systems is presented and applied in the flow-based temperature control in a long duct. At first, a radial basis function neural network is developed to estimate the temperature distribution along the duct with respect to flow velocity, assuming constant ambient temperature. The nonlinear model is then incorporated in the context of an MPC procedure. The use of the neural network model avoids the spatial discretization and decreases significantly the computational effort required to solve the optimization problem that is formulated in real time, compared to conventional modeling approaches. The proposed MPC scheme is able to overcome delay effects and accelerates the outlet temperature response. Reduced tuning effort is another advantage of the proposed control scheme.

Advanced Distillation Curve Measurement with a Model Predictive Temperature Controller

In previous work, several significant improvements in the measurement of distillation curves for complex fluids were introduced. The modifications to the classical measurement provide for (1) temperature and volume measurement(s) of low uncertainty, and most important, (2) a composition-explicit data channel in addition to the usual temperature–volume relationship. This latter modification is achieved with a new sampling approach that allows precise qualitative as well as quantitative analyses of each fraction, on the fly. In the new approach, the distillation temperature is measured in two locations. The temperature is measured in the usual location, at the bottom of the take-off in the distillation head, but it is also measured directly in the fluid. We have further modified our developmental instrument to incorporate a model predictive temperature controller. In response to either an equation-of-state calculation or a previous distillation curve, the programmable temperature controller increases the fluid temperature to achieve a constant mass flow rate of vapor through the distillation head. This approach eliminates the aberrations that one typically encounters in the data due to fluctuations in distillation rate, often referred to as hesitation. Thus, we can collect data from two temperature channels: one a true state point measurement (measured directly in the fluid) and the other comparable to previous data (measured in the head).

Enthalpy of Combustion of Fuels as a Function of Distillate Cut: Application of an Advanced Distillation Curve Method

In previous work, several significant improvements in the measurement of distillation curves for complex fluids were introduced. The modifications to the classical measurement provide for (1) temperature and volume measurements of low uncertainty, (2) temperature control based upon fluid behavior with a model predictive temperature controller, and, most important, (3) a composition-explicit data channel in addition to the temperature−volume relationship that usually comprises the measurement. This latter modification was achieved with a new sampling approach that allows precise qualitative as well as quantitative analyses of each fraction, during the measurement of the distillation curve. In this paper, we utilize the composition-explicit information to characterize distillate cuts in terms of available energy content. This is critical information in the study of real fuels. The measure we use for the fluid energy content is the composite enthalpy of combustion for each component selected for identification in each distillate fraction. As a test system, we present the distillation cuts of two prepared mixtures of n-decane + n-tetradecane. Then, as a further illustration of the approach, we present an analysis of distillate fractions of a 91 antiknock index (AI) gasoline and a 91 AI gasoline with 15% methanol (vol/vol) added.

Direct thermal dose control of constrained focused ultrasound treatments: phantom and in vivo evaluation

The first treatment control system that explicitly and automatically balances the efficacy and safety goals of noninvasive thermal therapies is described, and its performance is evaluated in phantoms and in vivo using ultrasound heating with a fixed, focused transducer. The treatment efficacy is quantified in terms of thermal dose delivered to the target. The developed feedback thermal dose controller has a cascade structure with the main nonlinear dose controller continuously generating the reference temperature trajectory for the secondary, constrained, model predictive temperature controller. The control system ensures thermal safety of the normal tissue by automatically complying with user-specified constraints on the maximum allowable normal tissue temperatures. To reflect hardware limitations and to prevent cavitation, constraints on the maximum transducer power can also be imposed. It is shown that the developed controller can be used to achieve the minimum-time delivery of the desired thermal dose to the target without violating safety constraints, which is a novel and clinically desirable feature. The developed controller is model based, and requires patient- and site-specific models for its operation. These models were obtained during pre-treatment identification experiments. In our implementation, predictive models, internally used by the automatic treatment controller, are dynamically updated each time new temperature measurements become available. The adaptability of internal models safeguards against adverse effects of modelling errors, and ensures robust performance of the control system in the presence of a priori unknown treatment disturbances. The successful validation with two experimental models of considerably different thermal and ultrasound properties suggests the applicability of the developed treatment control system to different anatomical sites.

Model predictive temperature control towards improving cement precalcination

Model predictive temperature control towards improving cement precalcination

Model predictive temperature control towards improving cement precalcination

In the present paper, the degree of precalcination in the precalciner of cement plants is increased via a proportional-integral (PI) model predictive abgasses temperature controller. The controller is based on polynomial neural network identification of the plant at a particular operating point as well as on a polynomial non-linear predictor depending on autoregressive identification predictors of temperature disturbances. According to simulation results, the degree of precalcination becomes significantly increased while energy consumption is reduced.