Rajagopalan Srinivasan

Abstract: In the face of highly competitive markets and constant pressure to reduce lead times, enterprises today consider supply chain management to be the key area where improvements can significantly impact the bottom line. More enterprises now consider the entire supply chain structure while taking business decisions. They try to identify and manage all critical relationships both upstream and downstream in their supply chains. Some impediments to this are that the necessary information usually resides across a multitude of resources, is ever changing, and is present in multiple formats. Most supply chain decision support systems (DSSs) are specific to an enterprise and its supply chain, and cannot be easily modified to assist other similar enterprises and industries. In this two-part paper, we propose a unified framework for modeling, monitoring and management of supply chains. The first part of the paper describes the framework while the second part illustrates its application to a refinery supply chain. The framework integrates the various elements of the supply chain such as enterprises, their production processes, the associated business data and knowledge and represents them in a unified, intelligent and object-oriented fashion. Supply chain elements are classified as entities, flows and relationships. Software agents are used to emulate the entities i.e. various enterprises and their internal departments. Flows—material and information—are modeled as objects. The framework helps to analyze the business policies with respect to different situations arising in the supply chain. We illustrate the framework by means of two case studies. A DSS for petrochemical cluster management is described together with a prototype DSS for crude procurement in a refinery.

Abstract: The refinery business involves tasks that span several departments and process large amount of data. Among others, these include crude procurement, logistics and scheduling (storage, distillation units, etc.). Current refinery decision support systems (DSSs) fail to integrate all the decision-making processes of a refinery, to interface with other systems in place, to incorporate dynamic data from various sources and to assist different departments concurrently. In part 1 of this two-part paper, we proposed an agent-based framework for supply chain DSSs. Here, we demonstrate its application through a prototype DSS, called petroleum refinery integrated supply chain modeler and simulator or PRISMS, for crude procurement. PRISMS serves as a central DSS through which all processes of a refinery can be studied and enables integrated decisions with respect to the overall refinery business. In particular, PRISMS can be used to study the effects of internal policies of the refinery and its various departments. We illustrate this through three detailed ‘what-if’ studies that provide an insight into how the business responds to changes in policies, exogenous events and plant modifications.

Abstract: In the petrochemical industry, the product quality reflects the commercial and operational performance of a manufacturing process. However, real-time measurement of product quality is generally difficult. Online prediction of quality using readily available, frequent process measurements would be beneficial in terms of operation and quality control. In this paper, a novel soft sensor technology based on partial least squares (PLS) regression is developed and applied to a refining process for quality prediction. The modeling process is described, with emphasis on data preprocessing, multivariate-outlier detection and variables selection. Enhancement of PLS strategy is also discussed for taking into account the dynamics in the process data. The proposed approach is applied to data from a refining process and the performance of the resulting soft sensor is evaluated by comparison with laboratory data and analyzer measurements.

Abstract: In today's competitive business climate characterized by uncertain oil markets, responding effectively and speedily to market forces, while maintaining reliable operations, is crucial to a refinery's bottom line. Optimal crude oil scheduling enables cost reduction by using cheaper crudes intelligently, minimizing crude changeovers, and avoiding ship demurrage. So far, only discrete-time formulations have stood up to the challenge of this important, nonlinear problem. A continuous-time formulation would portend numerous advantages, however, existing work in this area has just begun to scratch the surface. In this paper, we present the first complete continuous-time mixed integer linear programming (MILP) formulation for the short-term scheduling of operations in a refinery that receives crude from very large crude carriers via a high-volume single buoy mooring pipeline. This novel formulation accounts for real-world operational practices. We use an iterative algorithm to eliminate the crude composition discrepancy that has proven to be the Achilles heel for existing formulations. While it does not guarantee global optimality, the algorithm needs only MILP solutions and obtains excellent maximum-profit schedules for industrial problems with up to 7 days of scheduling horizon. We also report the first comparison of discrete- vs. continuous-time formulations for this complex problem.

Abstract: Scheduling of crude oil operations is a complex nonlinear problem, especially when tanks hold crude mixes. We present a new mixed-integer nonlinear programming (MINLP) formulation and a novel, mixed-integer linear programming (MILP)–based solution approach for optimizing crude oil unloading, storage, and processing operations in a multi-CDU (crude distillation unit) refinery receiving crude from multiparcel VLCCs (very large crude carriers) through a high-volume, single-buoy mooring (SBM) pipeline and/or single-parcel tankers through multiple jetties. Mimicking a continuous-time formulation, our primarily discrete-time model allows multiple sequential crude transfers to occur within a time slot. It incorporates several real-life operational features including brine settling and tank-to-tank transfers, and is superior to other reported models. Notably our algorithm avoids concentration discrepancy and MINLP solutions by identifying a part of the horizon, for which its linear relaxation is exact, and then solving this MILP repeatedly with progressively shorter horizons. By using 8 h time slots and a hybrid time representation, an attractive approach to this difficult problem is presented.© 2004 American Institute of Chemical Engineers AIChE J, 50:1177–1197, 2004

Abstract: An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of mutual-information-based registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, gray value interpolation, optimization, adaptations to the mutual information measure, and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects. Comparison studies including mutual information are also considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on past achievements and some future challenges.

Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Abstract: Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.

Abstract: Data-based process monitoring has become a key technology in process industries for safety, quality, and operation efficiency enhancement. This paper provides a timely update review on this topic. First, the natures of different industrial processes are revealed with their data characteristics analyzed. Second, detailed terminologies of the data-based process monitoring method are illustrated. Third, based on each of the main data characteristics that exhibits in the process, a corresponding problem is defined and illustrated, with review conducted with detailed discussions on connection and comparison of different monitoring methods. Finally, the relevant research perspectives and several promising issues are highlighted for future work.