The Experts below are selected from a list of 649845 Experts worldwide ranked by ideXlab platform
Michael Baldea - One of the best experts on this subject based on the ideXlab platform.
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minimized test times for step and pulse responses of slow linear Processes
Industrial & Engineering Chemistry Research, 2019Co-Authors: Friedrich Y Lee, Michael Baldea, Thomas F Edgar, Jietae LeeAbstract:Step and pulse responses have long been used to identify Process Dynamics and design control systems such as PID (proportional-integral-derivative) and model predictive control. They are simple and can represent different types of Process Dynamics intuitively. However, for some Processes, long test times in the open-loop environment are required to obtain them. Here, utilizing the time-optimal control techniques, methods to reduce test times significantly are investigated. From initial short-term responses, exact step and pulse responses are obtained without extrapolations or predictions. Since short-term responses are used, the proposed methods can also be used to restore step responses from fragments of failed step tests. Simulations and experiments are given to illustrate applicability of the proposed methods.
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an efficient milp framework for integrating nonlinear Process Dynamics and control in optimal production scheduling calculations
Computers & Chemical Engineering, 2018Co-Authors: Morgan T Kelley, Richard C Pattison, Ross Baldick, Michael BaldeaAbstract:Abstract The emphasis currently placed on enterprise-wide decision making and optimization has led to an increased need for methods of integrating nonlinear Process Dynamics and control information in scheduling calculations. The inevitable high dimensionality and nonlinearity of first-principles dynamic Process models makes incorporating them in scheduling calculations challenging. In this work, we describe a general framework for deriving data-driven surrogate models of the closed-loop Process Dynamics. Focusing on Hammerstein–Wiener and finite step response (FSR) model forms, we show that these models can be (exactly) linearized and embedded in production scheduling calculations. The resulting scheduling problems are mixed-integer linear programs with a special structure, which we exploit in a novel and efficient solution strategy. A polymerization reactor case study is utilized to demonstrate the merits of this method. Our framework compares favorably to existing approaches that embed Dynamics in scheduling calculations, showing considerable reductions in computational effort.
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moving horizon closed loop production scheduling using dynamic Process models
Aiche Journal, 2017Co-Authors: Richard C Pattison, Iiro Harjunkoski, Cara R Touretzky, Michael BaldeaAbstract:The economic circumstances that define the operation of chemical Processes (e.g., product demand, feedstock and energy prices) are increasingly variable. To maximize profit, changes in production rate and product grade must be scheduled with increased frequency. To do so, Process Dynamics must be considered in production scheduling calculations, and schedules should be recomputed when updated economic information becomes available. In this article, this need is addressed by introducing a novel moving horizon closed-loop scheduling approach. Process Dynamics are represented explicitly in the scheduling calculation via low-order models of the closed-loop Dynamics of scheduling-relevant variables, and a feedback connection is built based on these variables using an observer structure to update model states. The feedback rescheduling mechanism consists of, (a) periodic schedule updates that reflect updated price and demand forecasts, and, (b) event-driven updates that account for Process and market disturbances. The theoretical developments are demonstrated on the model of an industrial-scale air separation unit. © 2016 American Institute of Chemical Engineers AIChE J, 63: 639–651, 2017
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integrated production scheduling and model predictive control of continuous Processes
Aiche Journal, 2015Co-Authors: Michael Baldea, Juan Du, Jungup Park, Iiro HarjunkoskiAbstract:The integration of production management and Process control decisions is critical for improving economic performance of the chemical supply chain. A novel framework for integrating production scheduling and model predictive control (MPC) for continuous Processes is proposed. Our framework is predicated on using a low-dimensional time scale-bridging model (SBM) that captures the closed-loop Process Dynamics over the longer time scales that are relevant to scheduling calculations. The SBM is used as a constraint in a mixed-integer dynamic formulation of the scheduling problem. To synchronize the scheduling and MPC calculations, a novel scheduling-oriented MPC concept is proposed, whereby the SBM is incorporated in the expression of the controller as a (soft) dynamic constraint and allows for obtaining an explicit description of the closed-loop Process Dynamics. Our framework scales favorably with system size and provides desirable closed-loop stability and performance properties for the resulting integrated scheduling and control problem. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4179–4190, 2015
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a time scale bridging approach for integrating production scheduling and Process control
Computers & Chemical Engineering, 2015Co-Authors: Juan Du, Jungup Park, Iiro Harjunkoski, Michael BaldeaAbstract:Abstract In this paper, we propose a novel framework for integrating scheduling and nonlinear control of continuous Processes. We introduce the time scale-bridging model (SBM) as an explicit, low-order representation of the closed-loop input–output Dynamics of the Process. The SBM then represents the Process Dynamics in a scheduling framework geared towards calculating the optimal time-varying setpoint vector for the Process control system. The proposed framework accounts for Process Dynamics at the scheduling stage, while maintaining closed-loop stability and disturbance rejection properties via feedback control during the production cycle. Using two case studies, a CSTR and a polymerization reactor, we show that SBM-based scheduling has significant computational advantages compared to existing integrated scheduling and control formulations. Moreover, we show that the economic performance of our framework is comparable to that of existing approaches when a perfect Process model is available, with the added benefit of superior robustness to plant-model mismatch.
Nicola Fohrer - One of the best experts on this subject based on the ideXlab platform.
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Process verification of a hydrological model using a temporal parameter sensitivity analysis
Hydrology and Earth System Sciences, 2015Co-Authors: Matthias Pfannerstill, Bjorn Guse, Dominik E. Reusser, Nicola FohrerAbstract:To ensure reliable results of hydrological models, it is essential that the models reproduce the hydrological Process Dynamics adequately. Information about simulated Process Dynamics is provided by looking at the temporal sensitivities of the corresponding model parameters. For this, the temporal Dynamics of parameter sensitivity are analysed to identify the simulated hydrological Processes. Based on these analyses it can be verified if the simulated hydrological Processes match the observed Processes of the real world. We present a framework that makes use of Processes observed in a study catchment to verify simulated hydrological Processes. Temporal Dynamics of parameter sensitivity of a hydrological model are interpreted to simulated hydrological Processes and compared with observed hydrological Processes of the study catchment. The results of the analysis show the appropriate simulation of all relevant hydrological Processes in relation to Processes observed in the catchment. Thus, we conclude that temporal Dynamics of parameter sensitivity are helpful for verifying simulated Processes of hydrological models.
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detection of dominant nitrate Processes in ecohydrological modeling with temporal parameter sensitivity analysis
Ecological Modelling, 2015Co-Authors: Marcelo B Haas, Bjorn Guse, Matthias Pfannerstill, Nicola FohrerAbstract:Abstract River systems are impacted by nutrient inputs from the landscape. The transport of nitrate from agricultural areas into the river systems is related to numerous Processes, which occur simultaneously and influence each other permanently. Ecohydrological models aim to represent these complex nitrate Processes. For reliable model results, it is essential to better understand the nitrate Process Dynamics in models. This study aims to improve the understanding of nitrate Process Dynamics by using a temporal diagnostic model analysis. As diagnostic tool, a temporal parameter sensitivity analysis is applied on an ecohydrological model. With this method, phases of dominant model parameters are detected. The results show that the sensitivity of different nitrate parameters varies temporally. These temporal Dynamics in dominant parameters can be explained by temporal variations in nitrate transport and plant uptake Processes. A better view on the Dynamics of the temporal parameter sensitivity is obtained by analyzing different modeled runoff components and nitrate pathways. Thus, a temporal parameter sensitivity analysis assists the interpretation of seasonal variations in dominant nitrate pathways.
Dexian Huang - One of the best experts on this subject based on the ideXlab platform.
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concurrent monitoring of operating condition deviations and Process Dynamics anomalies with slow feature analysis
Aiche Journal, 2015Co-Authors: Chao Shang, Fan Yang, Johan A. K. Suykens, Xiaolin Huang, Dexian HuangAbstract:Latent variable (LV) models have been widely used in multivariate statistical Process monitoring. However, whatever deviation from nominal operating condition is detected, an alarm is triggered based on classical monitoring methods. Therefore, they fail to distinguish real faults incurring Dynamics anomalies from normal deviations in operating conditions. A new Process monitoring strategy based on slow feature analysis (SFA) is proposed for the concurrent monitoring of operating point deviations and Process Dynamics anomalies. Slow features as LVs are developed to describe slowly varying Dynamics, yielding improved physical interpretation. In addition to classical statistics for monitoring deviation from design conditions, two novel indices are proposed to detect anomalies in Process Dynamics through the slowness of LVs. The proposed approach can distinguish whether the changes in operating conditions are normal or real faults occur. Two case studies show the validity of the SFA-based Process monitoring approach. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3666–3682, 2015
Sam M Mannan - One of the best experts on this subject based on the ideXlab platform.
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the development and application of dynamic operational risk assessment in oil gas and chemical Process industry
Reliability Engineering & System Safety, 2010Co-Authors: Xiaole Yang, Sam M MannanAbstract:Abstract A methodology of dynamic operational risk assessment (DORA) is proposed for operational risk analysis in oil/gas and chemical industries. The methodology is introduced comprehensively starting from the conceptual framework design to mathematical modeling and to decision making based on cost–benefit analysis. The probabilistic modeling part of DORA integrates stochastic modeling and Process Dynamics modeling to evaluate operational risk. The stochastic system-state trajectory is modeled according to the abnormal behavior or failure of each component. For each of the possible system-state trajectories, a Process Dynamics evaluation is carried out to check whether Process variables, e.g., level, flow rate, temperature, pressure, or chemical concentration, remain in their desirable regions. Component testing/inspection intervals and repair times are critical parameters to define the system-state configuration, and play an important role for evaluating the probability of operational failure. This methodology not only provides a framework to evaluate the dynamic operational risk in oil/gas and chemical industries, but also guides the Process design and further optimization. To illustrate the probabilistic study, we present a case-study of a level control in an oil/gas separator at an offshore plant.
Chunhui Zhao - One of the best experts on this subject based on the ideXlab platform.
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Slow-Feature-Analysis-Based Batch Process Monitoring With Comprehensive Interpretation of Operation Condition Deviation and Dynamic Anomaly
IEEE Transactions on Industrial Electronics, 2019Co-Authors: Shumei Zhang, Chunhui ZhaoAbstract:In order to provide more sensitive monitoring results, the time Dynamics and steady-state operating conditions should be separately monitored by distinguishing time information from the steady-state counterpart. However, it is a more challenging task for batch Processes because they vary from phase to phase presenting multiple steady states and complex dynamic characteristics. To address the above issue, a concurrent monitoring strategy of multiphase steady states and Process Dynamics is developed for batch Processes in this paper. On one hand, multiple local models are constructed to identify a steady derivation from the normal operating condition for different phases. On the other hand, based on the recognition that the Process Dynamics can be considered to be irrelevant with the steady states, a global model is built to detect the Dynamics anomalies by monitoring the time variations. Corresponding to alarms issued by different statistics, different operating statuses are indicated with meaningful physical interpretation and deep Process understanding. To illustrate the feasibility and efficacy, the proposed algorithm is applied to the injection molding Process, which is a typical multiphase batch Process.
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Simultaneous Static and Dynamic Analysis for Fine-Scale Identification of Process Operation Statuses
IEEE Transactions on Industrial Informatics, 2019Co-Authors: Shumei Zhang, Chunhui Zhao, Biao HuangAbstract:Closed-loop control is commonly used in industrial Processes to track setpoints or regulate Process disturbances. Process Dynamics resulting from closed-loop control are reflected in data mainly in two aspects, namely serial correlation and variation of response speed. Concurrent analysis of both aspects from data has not been fully investigated in the literature. In this work, a combined strategy of canonical variate analysis and slow feature analysis is proposed to monitor Process Dynamics resulting from closed-loop control by exploring both serial correlations and variation speed of Process data. First, the canonical subspaces reflecting serial correlation are modeled by maximizing correlation between the past and future values of the Process data. Then, both the serially correlated canonical subspace and its residual subspace are further explored to extract the slow features, which are representations of Process variation speed. The proposed method provides a meaningful physical interpretation and in-depth Process analysis with considerations of Process Dynamics under closed-loop control. Besides, it provides a concurrent monitoring of both Process faults and operating condition deviations, resulting in fine-scale identification of different operation statuses. To demonstrate the feasibility and effectiveness, the proposed strategy is tested in a simulated typical chemical Process under closed-loop control, namely the three-phase flow Process.
