A brief report of the medical relief work done by Dr Daya and his team in Uttarkashi. Join us on fb to know more: https://www.facebook.com/DoctorsForSevaDFS
A Regularization Approach to the Reconciliation of Constrained Data SetsAlkis Vazacopoulos
A new iterative solution to the statistical adjustment of constrained data sets is derived in this paper. The method is general and may be applied to any weighted least squares problem containing nonlinear equality constraints. Other methods are available to solve this class of problem, but are complicated when unmeasured variables and model parameters are not all observable and the model constraints are not all independent. Of notable exception however are the methods of Crowe (1986) and Pai and Fisher (1988), although these implementations require the determination of a matrix projection at each iteration which may be computationally expensive. An alternative solution is proposed which makes the pragmatic assumption that the unmeasured variables and model parameters are known with a finite but equal uncertainty. We then re-formulate the well known data reconciliation solution in the absence of these unknowns to arrive at our new solution; hence the regularization approach. Another procedure for the classification of observable and redundant variables is also given which does not require the explicit computation of the matrix projection. The new algorithm is demonstrated using three illustrative examples previously used in other studies.
Phenomenological Decomposition Heuristics for Process Design Synthesis of Oil...Alkis Vazacopoulos
The processing of a raw material is a phenomenon that varies its quantity and quality along a specific network and logics and logistics to transform it into final products. To capture the production framework in a mathematical programming model, a full space formulation integrating discrete design variables and quantity-quality relations gives rise to large scale non-convex mixed-integer nonlinear models, which are often difficult to solve. In order to overcome this problem, we propose a phenomenological decomposition heuristic to solve separately in a first stage the quantity and logic variables in a mixed-integer linear model, and in a second stage the quantity and quality variables in a nonlinear programming formulation. By considering different fuel demand scenarios, the problem becomes a two-stage stochastic programming model, where nonlinear models for each demand scenario are iteratively restricted by the process design results. Two examples demonstrate the tailor-made decomposition scheme to construct the complex oil-refinery process design in a quantitative manner.
Improve Yield Accounting by including Density Measurements ExplicitlyAlkis Vazacopoulos
Today the growing standard in oil-refining yield accounting is to use statistical data reconciliation to assist in detecting and diagnosing malfunctioning flow and inventory meters and possible mis-specified oil movements. However, as we demonstrate in this article, potentially harmful and undetectable gross-errors can occur which may distort the yield accounting results and the overall health of the production balance. The solution is to reconcile both mass and volume simultaneously instead of reconciling mass or volume separately as is currently done. It is made possible by explicitly including density measurements into the reconciliation process and solving a bi-linear data reconciliation problem using off-the-shelf commercial software.
Presented in this short document is a description of what we call "Advanced" Property Tracking or Tracing (APT). APT is the term given to the technique of predicting, simulating, calculating or estimating the properties (i.e., densities, compositions, conditions, qualities, etc.) in a network or superstructure with significant inventory using statistical data reconciliation and regression (DRR)
Hierarchical Decomposition Heuristic for Scheduling: Coordinated Reasoning fo...Alkis Vazacopoulos
This paper presents a new technique for decomposing and rationalizing large decision-making problems into a common and consistent framework. We call this the hierarchical decomposition heuristic (HDH) which focuses on obtaining "globally feasible" solutions to the overall problem, i.e., solutions which are feasible for all decision-making elements in a system. The HDH is primarily intended to be applied as a standalone tool for managing a decentralized and distributed system when only globally consistent solutions are necessary or as a lower bound to a maximization problem within a global optimization strategy such as Lagrangean decomposition. An industrial scale scheduling example is presented that demonstrates the abilities of the HDH as an iterative and integrated methodology in addition to three small motivating examples. Also illustrated is the HDH's ability to support several types of coordinated and collaborative interactions.
Follow-Up Hearing Test in Long Island NY Recommended After National Hearing TestEast End Hearing
The audiologists at East End Hearing – Long Island Hearing Test Professionals agree that getting the word out about the National Hearing Test is important.
Full service audiologist with the best selection of hearing aids in Long Island NY. See us for hearing tests, custom ear protection, tinnitus treatment, ear wax removal, hearing aid repair.
A brief report of the medical relief work done by Dr Daya and his team in Uttarkashi. Join us on fb to know more: https://www.facebook.com/DoctorsForSevaDFS
A Regularization Approach to the Reconciliation of Constrained Data SetsAlkis Vazacopoulos
A new iterative solution to the statistical adjustment of constrained data sets is derived in this paper. The method is general and may be applied to any weighted least squares problem containing nonlinear equality constraints. Other methods are available to solve this class of problem, but are complicated when unmeasured variables and model parameters are not all observable and the model constraints are not all independent. Of notable exception however are the methods of Crowe (1986) and Pai and Fisher (1988), although these implementations require the determination of a matrix projection at each iteration which may be computationally expensive. An alternative solution is proposed which makes the pragmatic assumption that the unmeasured variables and model parameters are known with a finite but equal uncertainty. We then re-formulate the well known data reconciliation solution in the absence of these unknowns to arrive at our new solution; hence the regularization approach. Another procedure for the classification of observable and redundant variables is also given which does not require the explicit computation of the matrix projection. The new algorithm is demonstrated using three illustrative examples previously used in other studies.
Phenomenological Decomposition Heuristics for Process Design Synthesis of Oil...Alkis Vazacopoulos
The processing of a raw material is a phenomenon that varies its quantity and quality along a specific network and logics and logistics to transform it into final products. To capture the production framework in a mathematical programming model, a full space formulation integrating discrete design variables and quantity-quality relations gives rise to large scale non-convex mixed-integer nonlinear models, which are often difficult to solve. In order to overcome this problem, we propose a phenomenological decomposition heuristic to solve separately in a first stage the quantity and logic variables in a mixed-integer linear model, and in a second stage the quantity and quality variables in a nonlinear programming formulation. By considering different fuel demand scenarios, the problem becomes a two-stage stochastic programming model, where nonlinear models for each demand scenario are iteratively restricted by the process design results. Two examples demonstrate the tailor-made decomposition scheme to construct the complex oil-refinery process design in a quantitative manner.
Improve Yield Accounting by including Density Measurements ExplicitlyAlkis Vazacopoulos
Today the growing standard in oil-refining yield accounting is to use statistical data reconciliation to assist in detecting and diagnosing malfunctioning flow and inventory meters and possible mis-specified oil movements. However, as we demonstrate in this article, potentially harmful and undetectable gross-errors can occur which may distort the yield accounting results and the overall health of the production balance. The solution is to reconcile both mass and volume simultaneously instead of reconciling mass or volume separately as is currently done. It is made possible by explicitly including density measurements into the reconciliation process and solving a bi-linear data reconciliation problem using off-the-shelf commercial software.
Presented in this short document is a description of what we call "Advanced" Property Tracking or Tracing (APT). APT is the term given to the technique of predicting, simulating, calculating or estimating the properties (i.e., densities, compositions, conditions, qualities, etc.) in a network or superstructure with significant inventory using statistical data reconciliation and regression (DRR)
Hierarchical Decomposition Heuristic for Scheduling: Coordinated Reasoning fo...Alkis Vazacopoulos
This paper presents a new technique for decomposing and rationalizing large decision-making problems into a common and consistent framework. We call this the hierarchical decomposition heuristic (HDH) which focuses on obtaining "globally feasible" solutions to the overall problem, i.e., solutions which are feasible for all decision-making elements in a system. The HDH is primarily intended to be applied as a standalone tool for managing a decentralized and distributed system when only globally consistent solutions are necessary or as a lower bound to a maximization problem within a global optimization strategy such as Lagrangean decomposition. An industrial scale scheduling example is presented that demonstrates the abilities of the HDH as an iterative and integrated methodology in addition to three small motivating examples. Also illustrated is the HDH's ability to support several types of coordinated and collaborative interactions.
Follow-Up Hearing Test in Long Island NY Recommended After National Hearing TestEast End Hearing
The audiologists at East End Hearing – Long Island Hearing Test Professionals agree that getting the word out about the National Hearing Test is important.
Full service audiologist with the best selection of hearing aids in Long Island NY. See us for hearing tests, custom ear protection, tinnitus treatment, ear wax removal, hearing aid repair.