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Process Systems (process + system)
Selected AbstractsA Computerized Nursing Process Support System in BrazilINTERNATIONAL JOURNAL OF NURSING TERMINOLOGIES AND CLASSIFICATION, Issue 2003Maria da Graça Oliveira Crossetti BACKGROUND Hospital de Clinicas de Porto Alegre introduced the nursing process model as the basis for nursing practice at the hospital more than 20 years ago. A computerized nursing order system based on nursing diagnoses was introduced. The strategies used in the development of the system included establishment of Nursing Diagnosis Work Groups in 1998; systematic analysis of nursing processes based on the work of existing studies, the NANDA taxonomy in 1999; development and implementation of a data collection instrument to analyze the nursing diagnosis process; training of all nursing staff during 1999,2000; meetings between analysts and nursing staff to articulate the nursing process needs the system would be required to support; pilot implementation of the computerized nursing process system in the ICU in February 2000; and hospital-wide implementation in December 2000. The system supports nursing diagnoses and orders. It was developed in-house by the information systems group at the hospital and is implemented as an Oracle database accessed in client server mode over a Windows NT-based Ethernet network. The system is part of the hospital's larger clinical information management system. MAIN CONTENT POINTS The patient care module includes medical orders and nursing orders. On entering the nursing orders module, the user selects a patient and the system presents a list all current orders completed and pending. These orders can be examined, updated, and reprinted, and new daily nursing orders can also be input at this time. The "new order" screen provides the user with any previous orders to ensure consistency in nursing care. New nursing orders are prepared based on the patient history, physical exam, and daily evaluations. Required interventions are identified based on changes in the patient's "basic human needs." This process can be realized through two distinct paths through the nursing care module: one associated with diagnoses and the other with signs and symptoms. A nurse with more clinical experience and knowledge of diagnostic reasoning will opt to develop orders based on diagnoses. After the diagnosis and associated etiology is input, the system generates a list of possible interventions for selection. The duration and frequency of the intervention can then be specified and the order individualized to a patient's particular needs. Less experienced nurses and students will develop nursing orders based on a patient's signs and symptoms. The system generates a list of diagnoses, etiology, and associated basic human needs in response to the signs and symptoms input. The nurse selects the appropriate diagnoses and etiology and the system generates the list of nursing intervention options. Nurses following either path are required to confirm their orders. They then have the option of developing other orders for the same patient until all that patient's basic human needs have been addressed. The orders can be printed but also remain in the system for nursing staff to implement. CONCLUSIONS The application of systematic, evidence-based methods in nursing care results in improved quality of service that conforms to individual patients' basic human needs. [source] A multi-objective optimization approach to polygeneration energy systems designAICHE JOURNAL, Issue 5 2010Pei Liu Abstract Polygeneration, typically involving co-production of methanol and electricity, is a promising energy conversion technology which provides opportunities for high energy utilization efficiency and low/zero emissions. The optimal design of such a complex, large-scale and highly nonlinear process system poses significant challenges. In this article, we present a multiobjective optimization model for the optimal design of a methanol/electricity polygeneration plant. Economic and environmental criteria are simultaneously optimized over a superstructure capturing a number of possible combinations of technologies and types of equipment. Aggregated models are considered, including a detailed methanol synthesis step with chemical kinetics and phase equilibrium considerations. The resulting model is formulated as a non-convex mixed-integer nonlinear programming problem. Global optimization and parallel computation techniques are employed to generate an optimal Pareto frontier. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [source] A real-time monitoring photopolarimeter based on a multichannel signal process systemMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 12 2007Ruey-Ching Twu Abstract This study developed and evaluated a photopolarimeter based on a polarization interferometer with four photo detectors. By using the proposed multichannel signal process system, the time varying polarization states of an incident light beam, modulated by an electrically driving liquid crystal cell, can be measured instantly and monitored continually. Research findings contribute to the study of dynamic behaviors of the liquid crystal cell. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 3093,3096, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22947 [source] Dealing with uncertainty: adaptive approaches to sustainable river managementAQUATIC CONSERVATION: MARINE AND FRESHWATER ECOSYSTEMS, Issue 4 2002M.J. CLARKArticle first published online: 5 AUG 200 Abstract 1.Sustainable river management is the proclaimed aim of many agencies and institutions, but it remains challenging to bring this worthy ideal from the level of political rhetoric to that of practical river management. 2.Amongst the many drivers that already pressure the river manager, from internal institutional goals, through political aspirations to systemic change within the biophysical process system, one common element emerges, that of prevailing uncertainty. 3.Once it has been accepted that conventional science and engineering approaches to uncertainty (risk) minimization may be sub-optimal in a truly holistic (biophysical, socio-economic, political) system, the challenge emerges of developing a more appropriate framework without destroying over-burdened managers and management systems in the process. 4.It is argued that the necessary components are often already in place or under consideration. A linked model is proposed comprising practical measures of sustainability, robust approaches to uncertainty (if necessary, involving attitude change), responsive (adaptive) management frameworks, and an important underpinning of fuzzy decision support. Copyright © 2002 John Wiley & Sons, Ltd. [source] Resolution of two-way data from spectroscopic monitoring of reaction or process systems by parallel vector analysis (PVA) and window factor analysis (WFA): inspection of the effect of mass balance, methods and simulationsJOURNAL OF CHEMOMETRICS, Issue 3 2003Jian-Hui Jiang Abstract The effect of mass balance on the analysis of two-way data of reaction or process systems is investigated. It is shown that the rank-deficient species-related bilinear model can be converted to a full-rank reaction-related bilinear model, and in general situations the chemical rank for a system is the number of reactions plus one. Two slightly modified procedures are thus suggested to extract the spectral subspaces essential for resolution and to ascertain the number of reactions in different time domains. Based on the reaction-related bilinear model, a procedure of window factor analysis (WFA) is implemented for resolving the extent curves of reactions. A new two-way resolution approach, parallel vector analysis (PVA), is also developed. The idea of PVA is to construct a set of subspaces comprising only one common (spectral) component and then find a vector that is in parallel with a series of vectors coming from different subspaces. With suitably constructed subspaces the PVA procedure offers a versatile avenue to approach the unique resolution of spectral profiles. A four-component system which comprises four different processes or reactions is simulated. Results obtained reveal that favorable resolution is achieved for the spectral and concentration profiles by the suggested procedures of WFA and PVA. Copyright © 2003 John Wiley & Sons, Ltd. [source] Sequential and iterative architectures for distributed model predictive control of nonlinear process systemsAICHE JOURNAL, Issue 8 2010Jinfeng Liu Abstract In this work, we focus on distributed model predictive control of large scale nonlinear process systems in which several distinct sets of manipulated inputs are used to regulate the process. For each set of manipulated inputs, a different model predictive controller is used to compute the control actions, which is able to communicate with the rest of the controllers in making its decisions. Under the assumption that feedback of the state of the process is available to all the distributed controllers at each sampling time and a model of the plant is available, we propose two different distributed model predictive control architectures. In the first architecture, the distributed controllers use a one-directional communication strategy, are evaluated in sequence and each controller is evaluated only once at each sampling time; in the second architecture, the distributed controllers utilize a bi-directional communication strategy, are evaluated in parallel and iterate to improve closed-loop performance. In the design of the distributed model predictive controllers, Lyapunov-based model predictive control techniques are used. To ensure the stability of the closed-loop system, each model predictive controller in both architectures incorporates a stability constraint which is based on a suitable Lyapunov-based controller. We prove that the proposed distributed model predictive control architectures enforce practical stability in the closed-loop system and optimal performance. The theoretical results are illustrated through a catalytic alkylation of benzene process example. © 2010 American Institute of Chemical Engineers AIChE J, 2010 [source] A unified model of property integration for batch and continuous processesAICHE JOURNAL, Issue 7 2010Cheng-Liang Chen Abstract This article aims to present a general model for synthesis of property-based resource conservation networks. The proposed model is applicable to batch and continuous processes. Therein, the process systems are characterized by properties instead of composition that is found in most published works to date in the area of resource conservation. By treating continuous process as a special case of batch processes, both kinds of operations can be optimized with a unified model that is developed on the basis of a superstructure. The overall framework of property network is adopted, where material reuse/recycle, interception, and waste treatment are all taken into consideration. Apart from direct reuse/recycle, interception devices are employed to improve stream properties for further recovery, whereas effluent treatment is needed for compliance with environmental discharge limits. In addition, storage vessels are employed in batch processes to override intrinsic time constraint. Four case studies are solved to illustrate the proposed approach. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [source] Safe-steering of batch process systemsAICHE JOURNAL, Issue 11 2009Siam Aumi Abstract This work considers the problem of controlling batch processes to achieve a desired final product quality subject to input constraints and faults in the control actuators. Specifically, faults are considered that cannot be handled via robust control approaches, and preclude the ability to reach the desired end-point, necessitating fault-rectification. A safe-steering framework is developed to address the problem of determining how to utilize the functioning inputs during fault rectification to ensure that after fault-rectification, the desired product properties can be reached upon batch termination. To this end, first a novel reverse-time reachability region (we define the reverse time reachability region as the set of states from where the desired end point can be reached by batch termination) based MPC is formulated that reduces online computations, as well as provides a useful tool for handling faults. Next, a safe-steering framework is developed that utilizes the reverse-time reachability region based MPC in steering the state trajectory during fault rectification to enable (upon fault recovery) the achieving of the desired end point properties by batch termination. The proposed controller and safe-steering framework are illustrated using a fed-batch process example. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] Distributed model predictive control of nonlinear process systemsAICHE JOURNAL, Issue 5 2009Jinfeng Liu Abstract This work focuses on a class of nonlinear control problems that arise when new control systems which may use networked sensors and/or actuators are added to already operating control loops to improve closed-loop performance. In this case, it is desirable to design the pre-existing control system and the new control system in a way such that they coordinate their actions. To address this control problem, a distributed model predictive control method is introduced where both the pre-existing control system and the new control system are designed via Lyapunov-based model predictive control. Working with general nonlinear models of chemical processes and assuming that there exists a Lyapunov-based controller that stabilizes the nominal closed-loop system using only the pre-existing control loops, two separate Lyapunov-based model predictive controllers are designed that coordinate their actions in an efficient fashion. Specifically, the proposed distributed model predictive control design preserves the stability properties of the Lyapunov-based controller, improves the closed-loop performance, and allows handling input constraints. In addition, the proposed distributed control design requires reduced communication between the two distributed controllers since it requires that these controllers communicate only once at each sampling time and is computationally more efficient compared to the corresponding centralized model predictive control design. The theoretical results are illustrated using a chemical process example. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] Enhanced stability regions for model predictive control of nonlinear process systemsAICHE JOURNAL, Issue 6 2008Maaz Mahmood Abstract The problem of predictive control of nonlinear process systems subject to input constraints is considered. The key idea in the proposed approach is to use control-law independent characterization of the process dynamics subject to constraints via model predicative controllers to expand on the set of initial conditions for which closed,loop stability can be achieved. An application of this idea is presented to the case of linear process systems for which characterizations of the null controllable region (the set of initial conditions from where closed,loop stability can be achieved subject to input constraints) are available, but not practically implementable control laws that achieve stability from the entire null controllable region. A predictive controller is designed that achieves closed,loop stability for every initial condition in the null controllable region. For nonlinear process systems, while the characterization of the null controllable region remains an open problem, the set of initial conditions for which a (given) Lyapunov function can be made to decay is analytically computed. Constraints are formulated requiring the process to evolve within the region from where continued decay of the Lyapunov function value is achievable and incorporated in the predictive control design, thereby expanding on the set of initial conditions from where closed,loop stability can be achieved. The proposed method is illustrated using a chemical reactor example, and the robustness with respect to parametric uncertainty and disturbances demonstrated via application to a styrene polymerization process. © 2008 American Institute of Chemical Engineers AIChE J, 2008 [source] Fault-tolerant control of process systems using communication networksAICHE JOURNAL, Issue 6 2005Nael H. El-Farra Abstract A methodology for the design of fault-tolerant control systems for chemical plants with distributed interconnected processing units is presented. Bringing together tools from Lyapunov-based nonlinear control and hybrid systems theory, the approach is based on a hierarchical architecture that integrates lower-level feedback control of the individual units with upper-level logic-based supervisory control over communication networks. The local control system for each unit consists of a family of control configurations for each of which a stabilizing feedback controller is designed and the stability region is explicitly characterized. The actuators and sensors of each configuration are connected, via a local communication network, to a local supervisor that orchestrates switching between the constituent configurations, on the basis of the stability regions, in the event of failures. The local supervisors communicate, through a plant-wide communication network, with a plant supervisor responsible for monitoring the different units and coordinating their responses in a way that minimizes the propagation of failure effects. The communication logic is designed to ensure efficient transmission of information between units, while also respecting the inherent limitations in network resources by minimizing unnecessary network usage and accounting explicitly for the effects of possible delays due to fault-detection, control computations, network communication and actuator activation. The proposed approach provides explicit guidelines for managing the various interplays between the coupled tasks of feedback control, fault-tolerance and communication. The efficacy of the proposed approach is demonstrated through chemical process examples. © 2005 American Institute of Chemical Engineers AIChE J, 2005 [source] Attainable reaction and separation processes from a superstructure-based methodAICHE JOURNAL, Issue 6 2003Patrick Linke Generic technology for the synthesis and optimization of integrated reaction and separation systems uses rich superstructure formulations comprising two types of generic synthesis units with flexible representation modes. A reactor/mass exchanger unit enables a detailed representation of the reaction and mass exchange phenomena. A conceptual representation of separation systems is facilitated through separation task units. All possible process designs featuring reaction, reactive separation, and separation are embedded in the superstructure formulations as combinations of generic units and their features. The design options are explored using stochastic optimization techniques suitable for this class of problems. The flexible representation framework enables technology applications to general process design, as well as design subproblems including reactor and reactive separator design. Four case studies demonstrate the ability of the methodology to address a wide variety of process systems and to deliver design novelty. [source] Hamiltonian view on process systemsAICHE JOURNAL, Issue 8 2001Katalin M. Hangos The thermodynamic approach of analyzing structural stability of process plants was extended to construct the simple Hamiltonian model of lumped process systems. This type of model enables us to design a nonlinear PD feedback controller for passivation and loop shaping. This approach is applicable for lumped process systems where Kirchhoff convective transport takes place together with the transfer and sources of various types, and the manipulable input variables are the flow rates. Systems with constant mass holdup and uniform pressure in every balance volume satisfy these conditions. General results are shown by simple examples of practical importance: on a bilinear heat exchanger cell and on an isotherm CSTR with nonlinear reaction. [source] Passive control design for distributed process systems: Theory and applicationsAICHE JOURNAL, Issue 8 2000Antonio A. Alonso A recently developed theory linking passivity with the second law of thermodynamics was used to develop a robust control design methodology for process systems with states distributed in time and space. Asymptotic stabilization of the infinite dimensional state thus can be accomplished for convection,diffusion processes with nonlinear production terms. Two examples representative of these phenomena were considered: chemical reactors and thermal treatments induced by electromagnetic fields. The first case shows how mixing and reactor size are critical control design parameters. If mixing is complete, at least in some direction, exponential stabilization can be achieved by high gain control. If not, stabilization is still possible for reaction domains smaller than a critical volume. In the second case, the electromagnetic field power supplied can always be manipulated to preserve passivity for any domain size. Two important consequences are that the infinite dimensional state can be reconstructed at arbitrary precision by robust observers and that control of the energy inventory will suffice to provide asymptotic stabilization. Theoretical justification of these findings is given on a general framework and illustrated through simulation experiments. [source] Dynamic Process Modelling using a PCA-based Output Integrated Recurrent Neural NetworkTHE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 4 2002Yu Qian Abstract A new methodology for modelling of dynamic process systems, the output integrated recurrent neural network (OIRNN), is presented in this paper. OIRNN can be regarded as a modified Jordan recurrent neural network, in which the past values for certain steps of the output variables are integrated with the input variables, and the original input variables are pre-processed using principal component analysis (PCA) for the purpose of dimension reduction. The main advantage of the PCA-based OIRNN is that the input dimension is reduced, so that the network can be used to model the dynamic behavior of multiple input multiple output (MIMO) systems effectively. The new method is illustrated with reference to the Tennessee-Eastman process and compared with principal component regression and feedforward neural networks. On présente dans cet article une nouvelle méthodologie pour la modélisation de systèmes de procédés dynamiques, soit le réseau neuronal récurrent avec intégration de la réponse (OIRNN). Ce dernier peut être vu comme un réseau neuronal récurrent de Jordan modifié, dans lequel les valeurs passées pour certaines étapes des valeurs de sortie sont intégrées aux variables d'entrée et les variables d'entrée originales pré-traitée par l'analyse des composants principaux (PCA) dans un but de réduction des dimensions. Le principal avantage de l'OIRNN basé sur la PCA est que la dimension d'entée est réduite de sorte que le réseau peut servir à modéliser le comportement dynamique de systèmes à entrée et sorties multiples (MIMO) de façon efficace. La nouvell méthod est illustrée dans le cas du procédé Tennessee-Eastman et est comparée aux réseaux neuronaux anticipés et à régression des composants principaux. [source] | ||||||||||