The Experts below are selected from a list of 1051173 Experts worldwide ranked by ideXlab platform
Ruud B M Huirne - One of the best experts on this subject based on the ideXlab platform.
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An Economic Model to calculate farm-specific losses due to bovine respiratory disease in dairy heifers.
Preventive veterinary medicine, 2001Co-Authors: H.j. Van Der Fels-klerx, J T Sørensen, A W Jalvingh, Ruud B M HuirneAbstract:This paper describes a personal-computer-based Model estimating the Economic losses associated with clinical bovine respiratory disease in replacement heifers raised on individual dairy farms. The Model is based on the partial-budgeting technique, and calculates the losses for two types of the disease separately: calf pneumonia and a seasonal outbreak. Model input includes farm-specific data such as the incidence of bovine respiratory disease, prices, and effects of the disease on the heifers' productivity. The input database was linked directly with the Economic Model. For all input parameters, default values used are available to the user and can be modified easily. Losses considered by the Model include treatment expenditures and costs associated with increased mortality, increased premature culling, reduced growth, reduced fertility and reduced milk production in first lactation. Uncertainty is taken into account for parameters related to disease incidence, mortality and culling.Basic calculations for a typical Dutch dairy farm with 60% of the heifers (
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an Economic Model to calculate farm specific losses due to bovine respiratory disease in dairy heifers
Preventive Veterinary Medicine, 2001Co-Authors: H J Van Der Felsklerx, J T Sørensen, A W Jalvingh, Ruud B M HuirneAbstract:Abstract This paper describes a personal-computer-based Model estimating the Economic losses associated with clinical bovine respiratory disease in replacement heifers raised on individual dairy farms. The Model is based on the partial-budgeting technique, and calculates the losses for two types of the disease separately: calf pneumonia and a seasonal outbreak. Model input includes farm-specific data such as the incidence of bovine respiratory disease, prices, and effects of the disease on the heifers’ productivity. The input database was linked directly with the Economic Model. For all input parameters, default values used are available to the user and can be modified easily. Losses considered by the Model include treatment expenditures and costs associated with increased mortality, increased premature culling, reduced growth, reduced fertility and reduced milk production in first lactation. Uncertainty is taken into account for parameters related to disease incidence, mortality and culling. Basic calculations for a typical Dutch dairy farm with 60% of the heifers ( 31.2 per heifer present on the farm (range 18.4–57.1). The estimated losses for one seasonal outbreak with heifers up to 15-months old affected were 27.0 per heifer present (range 17.2–43.1). For both BRD types, the Model’s outcome was most sensitive to the number of heifers affected. Most of the parameters that had a major impact on the total losses were related to treatment or to the effects on the heifers’ productivity. The Model is user-friendly and flexible, and can be used as an interactive tool by farmers and veterinarians in the (Economic) decision-making process regarding on-farm prevention and control of bovine respiratory disease.
Panagiotis D Christofides - One of the best experts on this subject based on the ideXlab platform.
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Economic Model Predictive Control: Handling Valve Actuator Dynamics and Process Equipment Considerations
Foundations and Trends® in Systems and Control, 2018Co-Authors: Helen Durand, Panagiotis D ChristofidesAbstract:Economic Model Predictive Control: Handling Valve Actuator Dynamics and Process Equipment Considerations
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fault tolerant Economic Model predictive control using empirical Models financial support from the national science foundation and the department of energy is gratefully acknowledged
IFAC-PapersOnLine, 2017Co-Authors: Anas Alanqar, Helen Durand, Panagiotis D ChristofidesAbstract:Abstract In this work, we present a data-driven methodology to overcome actuator faults using a Model-based feedback controller that optimizes process Economics termed Economic Model predictive control (EMPC). Specifically, we utilize a moving horizon error detector that quantifies prediction errors and triggers updating the empirical Model used for state predictions in the EMPC on-line using the most recent input/output data collected after the fault when significant prediction errors occur due to the loss of an actuator. The proposed approach is applied to a catalytic chemical reactor example where an actuator fault occurs, affecting the coolant temperature. The proposed scheme was able to reduce prediction errors caused by the actuator loss by replacing the Model within the EMPC with a more accurate Model, resulting in improved Economic performance compared to not updating the Model.
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Economic Model predictive control of nonlinear process systems using empirical Models
Aiche Journal, 2015Co-Authors: Anas Alanqar, Matthew J Ellis, Panagiotis D ChristofidesAbstract:Economic Model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate Economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process Economics. As its name implies, EMPC requires the availability of a dynamic Model to compute its control actions and such a Model may be obtained either through application of first principles or through system identification techniques. In industrial practice, it may be difficult in general to obtain an accurate first-principles Model of the process. Motivated by this, in the present work, Lyapunov-based EMPC (LEMPC) is designed with a linear empirical Model that allows for closed-loop stability guarantees in the context of nonlinear chemical processes. Specifically, when the linear Model provides a sufficient degree of accuracy in the region where time varying Economically optimal operation is considered, conditions for closed-loop stability under the LEMPC scheme based on the empirical Model are derived. The LEMPC scheme is applied to a chemical process example to demonstrate its closed-loop stability and performance properties as well as significant computational advantages. © 2014 American Institute of Chemical Engineers AIChE J, 61: 816–830, 2015
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a tutorial review of Economic Model predictive control methods
Journal of Process Control, 2014Co-Authors: Matthew J Ellis, Helen Durand, Panagiotis D ChristofidesAbstract:An overview of the recent results on Economic Model predictive control (EMPC) is presented and discussed addressing both closed-loop stability and performance for nonlinear systems. A chemical process example is used to provide a demonstration of a few of the various approaches. The paper concludes with a brief discussion of the current status of EMPC and future research directions to promote and stimulate further research potential in this area.
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Economic Model predictive control with time varying objective function for nonlinear process systems
Aiche Journal, 2014Co-Authors: Matthew J Ellis, Panagiotis D ChristofidesAbstract:Economic Model predictive control (EMPC) is a control scheme that combines real-time dynamic Economic process optimization with the feedback properties of Model predictive control (MPC) by replacing the quadratic cost function with a general Economic cost function. Almost all the recent work on EMPC involves cost functions that are time invariant (do not explicitly account for time-varying process Economics). In the present work, we focus on the development of a Lyapunov-based EMPC (LEMPC) scheme that is formulated with an explicitly time-varying Economic cost function. First, the formulation of the proposed two-mode LEMPC is given. Second, closed-loop stability is proven through a theoretical treatment. Last, we demonstrate through extensive closed-loop simulations of a chemical process that the proposed LEMPC can achieve stability with time-varying Economic cost as well as improve Economic performance of the process over a conventional MPC scheme. © 2013 American Institute of Chemical Engineers AIChE J 60: 507–519, 2014
Frank Allgower - One of the best experts on this subject based on the ideXlab platform.
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on necessity and robustness of dissipativity in Economic Model predictive control
IEEE Transactions on Automatic Control, 2015Co-Authors: Matthias A Muller, David Angeli, Frank AllgowerAbstract:In this paper, we study a dissipativity property which was recently used in several results on Economic Model predictive control to ensure optimal operation of a system at steady-state as well as stability. In particular, we first investigate whether this dissipativity property is not only sufficient, but also necessary for optimal steady-state operation. In the most general case, this is not true; nevertheless, under an additional controllability assumption, we show that dissipativity is in fact necessary. Second, we provide a robustness analysis of the dissipativity property with respect to changes in the constraint set, which can result in a change in the considered supply rate.
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Economic Model predictive control with self tuning terminal cost
European Journal of Control, 2013Co-Authors: Matthias A Muller, David Angeli, Frank AllgowerAbstract:Abstract In this paper, we propose an Economic Model predictive control (MPC) framework with a self-tuning terminal weight, which builds on a recently proposed MPC algorithm with a generalized terminal state constraint. First, given a general time-varying terminal weight, we derive an upper bound on the closed-loop average performance which depends on the limit value of the predicted terminal state. After that, we derive conditions for a self-tuning terminal weight such that bounds for this limit value can be obtained. Finally, we propose several update rules for the self-tuning terminal weight and analyze their respective properties. We illustrate our findings with several examples.
Anastasia Ailamaki - One of the best experts on this subject based on the ideXlab platform.
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An Economic Model for self-tuned cloud caching
Proceedings - International Conference on Data Engineering, 2009Co-Authors: Debabrata Dash, Verena Kantere, Anastasia AilamakiAbstract:Cloud computing, the new trend for service infrastructures requires user multi-tenancy as well as minimal capital expenditure. In a cloud that services large amounts of data that are massively collected and queried, such as scientific data, users typically pay for query services. The cloud supports caching of data in order to provide quality query services. User payments cover query execution costs and maintenance of cloud infrastructure, and incur cloud profit. The challenge resides in providing efficient and resource-Economic query services while maintaining a profitable cloud. In this work we propose an Economic Model for self-tuned cloud caching targeting the service of scientific data. The proposed economy is adapted to policies that encourage high-quality individual and overall query services but also brace the profit of the cloud. We propose a cost Model that takes into account all possible query and infrastructure expenditure. The experimental study proves that the proposed solution is viable for a variety of workloads and data.
Santiago Grijalva - One of the best experts on this subject based on the ideXlab platform.
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HICSS - An Economic Model for Distributed Energy Prosumers
2013 46th Hawaii International Conference on System Sciences, 2013Co-Authors: Qin Sun, Aaron Beach, Michael E. Cotterell, Santiago GrijalvaAbstract:In order to optimally schedule energy, consumers need to understand their energy utility function. The traditional utility function must be modified when consumers acquire production capabilities and become prosumers. This paper presents a formal Economic Model for prosumers, appropriately integrating distributed generation, storage and demand response capabilities. We identify the limitations of existing prosumer-like Models. Finally, we demonstrate how the proposed prosumer Model is applied to energy evaluation to distributed prosumer decisions regarding environmental objectives.
