Yiping Feng

Simulation platform in the virtual factory laboratory system

Abstract A process simulation platform of a virtual factory laboratory system and its application to investigation of PCS and MES for process industries are introduced in this paper. Based on a dynamic simulation of a crude oil distillation process, we provide a detailed analysis of the development of dynamic-static hybrid simulation platform, which combines dynamic simulation of key units with static simulation of the whole industrial process and would help solve the problem of simulation efficiency. A hardware-in-the-loop (HIL) simulation is also presented by replacing the distillation unit with a real pilot distillation tower in the virtual factory laboratory system.

Virtual plant laboratory system of process industries for education

Abstract A kind of virtual plant laboratory system for process systems engineering education and research is developed, which is deemed an effective and applicable tool. The proposed system consists of pilot tanks, package simulation software of unit operation and a real enterprise information system including DCS, management database and MES (manufacturing execution system). Particularly, positive effects of customizing the virtual education systems to satisfy the needs of specific engineering education and research domain such as modeling, optimization and control of industrial plant, and development and implementation of enterprise information systems are highlighted.

VIRTUAL FACTORY LABORATORY SYSTEM AND ITS APPLICATION

Abstract This paper proposes a novel virtual factory laboratory system which includes enterprise applications and process modeling and simulation. The virtual factory is an enterprise-wide reference model for process industry which can simulate the production and the enterprise operation in many situations. Process simulation platform in the virtual factory is an open environment for modeling and simulating different processes, while enterprise application platform supports operation management research. User interface of this system, which presents physical model and information of enterprise, is built with virtual reality (VR) and visualization technique.

Optimizing Crude Oil Operations under Uncertainty

Abstract This paper presents a novel approach for refinery crude oil operations under uncertainty. Due to the flexibility of the crude oil scheduling, decisions made by deterministic optimizations are often conservative or lack of robustness or even infeasible, so in this paper future uncertainties are considered to improve feasibility and robustness of the schedule. To handle fluctuating product demand and uncertain ship arrival time, deterministic formulation is replaced by chance constrained programming. Through a series of examples, it proves that by using probabilistic programming, the solution of the problem provides a more robust scheduling under a comprehensive confidence level. The relationship between the probability and reliability of a planned operation is also discussed.

A Process Control Platform for Education in the Virtual Factory Laboratory System

Abstract This paper describes an experimental platform which is useful for graduate and undergraduate education in control engineering. It contains a six-tank liquid level regulation system and a pilot distillation column, which can be used as stand-alone apparatus. Some extensions of the apparatus are made to increase the function of the platform. The compositions of distillate can be estimated by adding soft sensors to the distillation column. Integrating with liquid level regulation system makes the inlet and outlet flow of the distillation column controllable which provides a realistic engineering experimental environment. It is possible to describe the impacts of unloading from upstream and charging to downstream as in process industry. The platform has been used for graduate courses such as system identification, soft sensor designing and advanced control system.

An approach to gross error detection based on the residual of single node

Measurements such as flow rates from a chemical process are inherently inaccurate. They are contaminated by random errors and possibly gross errors such as process disturbances, leaks, departure from steady state, and biased instrumentation. These measurements violate conservation laws and other process constraints. Data reconciliation aims at estimating the true values of measured variables that are consistent with the constraints, detecting gross errors, and solving for unmeasured variables. The problem of gross error detection and identification became the bottleneck of data reconciliation. A new approach for gross error identification based on the reliability and precision of the flow meters, as well as the residual of a single node, is presented. Simulations are given and a comparison is made between the new approach and some other widely used methods. It is shown that the proposed method is quite effective in gross error identification, especially when the system comprises a relatively large number of measurements.

Dynamic material balancing: A simplified least squares formulation

In this work, we propose a simplified least squares formulation (SLSF) for dynamic material balancing in chemical processes, which are often described by differential-algebraic equations. We compare the SLSF with traditional techniques, such as steady state data reconciliation (SSDR) and Kalman filter (KF). We also modify the SLSF when its assumptions cant be totally satisfied in some practical settings. Using chemical systems examples, we demonstrate that the SLSF can well deal with the practical dynamic material balancing problems.

An Improved MILP Method for Data Rectifications with Gross Error Candidates

Abstract MILP (Mixed Integer Linear Programming) method for simultaneous gross error detection and data reconciliation has been proved to be an efficient way to adjust process data with material and other balance constraints. But the efficiency will decrease significantly when the MILP method is applied in a large-scale data rectification problem because there are too many binary variables to be considered. In this paper, a strategy is proposed to generate a list of gross error candidates with reliability factors. The list of candidates are combined into the MILP objective function to improve the efficiency and accuracy through reducing the number of binary variables and giving accurate weights for suspected gross errors. Industrial examples are provided to show the efficiency of the algorithm.

A HYBRID PLATFORM FOR REFINERY SIMULATION WITH CASE SWITCHES

Abstract A new process simulation platform for refinery plant simulation and its application are introduced in this paper. Based on simulation of a crude oil distillation process, we provide a detail analysis of Flexible Multi-Case Data-drive Simulation (FMCDS), which employs the idea of discrete event simulation and would be helpful for the improvement of simulation accuracy and efficiency. The methodology used to mining the similar cases successfully reduce the number of unnecessary cases and provide a reliable case list for the platform. And a comparison between the proposed model and the static model is also presented by checking the degree of fitting between simulation data and actual plant data.