Computer-aided protective system hazard analysis

Based on a fault tree for a plant and on two fault trees for its protective system, the computer code, PROTECT, produces time profiles of expected numbers of normal trips, spurious trips, and destructive hazards. Input parameters to PROTECT are (1) minimal cut sets of the fault trees; (2) mean time to failures of basic components; (3) failure characteristics of operators; (4) scheduled maintenance intervals for plant and protective system; and (5) repair times from normal and spurious trips. Using the code, we can design the protective systems and maintenance policies, keeping both destructive hazards and spurious trips below acceptable levels.     

数控注塑成型机控制装置的设计

利用可编程控制器,单片机组成的控制系统对数控注塑成型机改造,人工健盘输入设定加工工艺就程,速度,时间周期等参数,具有完善的指示,故障声光报警功能,外围电路简单,控制系统可靠。     

基于多元时滞序列驱动的复杂过程故障预测方法应用研究

复杂过程故障预测是保证过程安全可靠运行的关键,而复杂系统的工作状态往往由多元时滞序列决定,该序列含有变量间的时滞信息及相关关系,具有一定的信息完备性。因此文章提出一种基于多元时滞序列驱动的复杂过程故障预测方法,该方法首先构建复杂系统的时滞符号有向图（TD-SDG）进而得到多元时滞序列,然后针对复杂系统变量多、关系复杂的问题,提出一种独立成分分析（ICA）和ELM神经网络集成的方法,此方法可快速获取多元时滞序列的独立成分从而建立监控统计量,最终达到故障预测的目的。通过在Tennessee Eastman（TE）过程上的仿真实验验证,表明所提方法能够至少提前15 min预测到故障,方便工作人员及时有效地采取措施。     

炼化装置故障链式效应定量安全预警方法

炼化装置故障及其故障链式效应对油气生产和人民生命安全所造成的危害严重。从故障链角度进行事故风险研究,提出炼化装置故障链式效应定量安全预警方法。首先分析炼化装置故障链式关系结构,基于目标树成功树-动态主逻辑图（GTST-DMLD）建立其故障链式效应关系模型,揭示炼化装置故障链式效应行为规律和关联本质,从而评价装置异常工况下的安全状态。进一步以马尔可夫过程为理论基础,建立故障链式效应预测模型,预测故障传播的后果和方向,并计算各后果的发生概率,为现场操作人员进行主动维修或应急处置提供依据。案例分析中通过对某化工厂常压塔装置、减压炉装置为研究对象进行应用与验证,结果表明该方法可以准确地对系统故障发生后的状态进行评价和预测,方法有效、可行,便于操作人员在处置已有故障的同时,注意预防其他异常工况的发生,降低油气生产加工过程中的整体风险。     

Dynamic risk analysis using alarm databases to improve process safety and product quality: Part I—Data compaction

In most industrial processes, vast amounts of data are recorded through their distributed control systems (DCSs) and emergency shutdown (ESD) systems. This two‐part article presents a dynamic risk analysis methodology that uses alarm databases to improve process safety and product quality. The methodology consists of three steps: (i) tracking of abnormal events over an extended period of time, (ii) event‐tree and set‐theoretic formulations to compact the abnormal‐event data, and (iii) Bayesian analysis to calculate the likelihood of the occurrence of incidents. Steps (i) and (ii) are presented in Part I and step (iii) in Part II. The event‐trees and set‐theoretic formulations allow compaction of massive numbers (millions) of abnormal events. For each abnormal event, associated with a process or quality variable, its path through the safety or quality systems designed to return its variable to the normal operation range is recorded. Event trees are prepared to record the successes and failures of each safety or quality system as it acts on each abnormal event. Over several months of operation, on the order of 10⁶ paths through event trees are stored. The new set‐theoretic structure condenses the paths to a single compact data record, leading to significant improvement in the efficiency of the probabilistic calculations and permitting Bayesian analysis of large alarm databases in real time. As a case study, steps (i) and (ii) are applied to an industrial, fluidized‐catalytic‐cracker. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

A practical approach to improve alarm system performance: Application to power plant

Process safety in chemical industries is considered to be one of the important goals towards sustainable development. This is due to the fact that, major accidents still occur and continue to exert significant reputational and financial impacts on process industries. Alarm systems constitute an indispensable component of automation as they draw the attention of process operators to any abnormal condition in the plant. Therefore, if deployed properly, alarm systems can play a critical role in helping plant operators ensure process safety and profitability. However, in practice, many process plants suffer from poor alarm system configuration which leads to nuisance alarms and alarm floods that compromise safety. A vast amount of research has primarily focused on developing sophisticated alarm management algorithms to address specific issues. In this article, we provide a simple, practical, systematic approach that can be applied by plant engineers(i.e., non-experts) to improve industrial alarm system performance. The proposed approach is demonstrated using an industrial power plant case study.     

Research on fault detection method for heat pump air conditioning system under cold weather

Building energy consumption accounts for nearly 40% of global energy consumption, HVAC (Heating, Ventilating, and Air Conditioning) systems are the major building energy consumers, and as one type of HVAC systems, the heat pump air conditioning system, which is more energy-efficient compared to the traditional air conditioning system, is being more widely used to save energy. However, in northern China, extreme climatic conditions increase the cooling and heating load of the heat pump air conditioning system and accelerate the aging of the equipment, and the sensor may detect drifted parameters owing to climate change. This non-linear drifted parameter increases the false alarm rate of the fault detection and the need for unnecessary troubleshooting. In order to overcome the impact of the device aging and the drifted parameter, a Kalman filter and SPC (statistical process control) fault detection method are introduced in this paper. In thismethod, themodel parameter and its standard variance can be estimated by Kalman filter based on the gray model and the real-time data of the air conditioning system. Further, by using SPC to construct the dynamic control limits, false alarm rate is reduced. And this paper mainly focuses on the cold machine failure in the component failure and its soft fault detection. This approach has been tested on a simulation model of the “Sino-German Energy Conservation Demonstration Center” building heat pump air-conditioning system in Shenyang, China, and the results show that the Kalman filter and SPC fault detection method is simple and highly efficient with a low false alarm rate, and it can deal with the difficulties caused by the extreme environment and the non-linear influence of the parameters, and what's more, it provides a good foundation for dynamic fault diagnosis and fault prediction analysis.     

Real-time analysis of process plant alarms using a mini-computer

Many process plants are equipped with alarm systems. In most cases the operator is left to perform an overall diagnosis from a pattern of alarms. He is expected to perform this analysis in real time, while under considerable stress, even though the fault sequence which has caused the pattern of alarms may be one which he has never seen before. The objective of the work reported here is to investigate the use of a digital computer to assist the operator in diagnosing the basic fault from a pattern of alarms.     

Design and assessment of delay timer alarm systems for nonlinear chemical processes

In process and manufacturing industries, alarm systems play a critical role in ensuring safe and efficient operations. The objective of a standard industrial alarm system is to detect undesirable deviations in process variables as soon as they occur. Fault detection and diagnosis systems often need to be alerted by an industrial alarm system; however, poorly designed alarms often lead to alarm flooding and other undesirable events. In this article, we consider the problem of industrial alarm design for processes represented by stochastic nonlinear time‐series models. The alarm design for such complex processes faces three important challenges: (1) industrial processes exhibit highly nonlinear behavior; (2) state variables are not precisely known (modeling error); and (3) process signals are not necessarily Gaussian, stationary or uncorrelated. In this article, a procedure for designing a delay timer alarm configuration is proposed for the process states. The proposed design is based on minimization of the rate of false and missed alarm rates—two common performance measures for alarm systems. To ensure the alarm design is robust to any non‐stationary process behavior, an expected‐case and a worst‐case alarm designs are proposed. Finally, the efficacy of the proposed alarm design is illustrated on a non‐stationary chemical reactor problem. © 2017 American Institute of Chemical Engineers AIChE J, 63: 77–90, 2018     

Computer-aided analysis and design of robust multivariable control systems for chemical processes

A conceptual framework to design robust process control systems is develope d and its realization through an interactive computer-aided design software is presented. The overall design methodology is based on a unified treatment of recent theoretical results in modern control and new computational techniques in symbolic logic manipulation, singular value decomposition and optimization. Several physical examples are given to demonstrate the application of the design approach and the utility of its computer software.     

考虑人因指标的过程报警阈值自适应优化方法

目前大多过程参数报警阈值优化方法没有考虑操作员的人因影响,为此,结合人因工程的理论,提出了一种考虑操作员人因影响的过程报警阈值自适应优化方法。首先,采用APRIORI算法挖掘报警事件与操作事件的关联关系;基于在线报警数据,模糊推理获得人因综合指标;根据误报、漏报与人因指标的关系计算两者的权重,建立报警阈值优化目标函数。工业实例数据验证表明,此方法对于过程报警阈值的优化设置具有较好的人因环境适应性,能够使得过程报警系统的性能得以进一步的改善。     

Structure and function at catalyst surfaces

New advances in theory and experiment have greatly increased our ability to study structure at surfaces. Not only can complex ordered structures of adsorbates be determined, but we can bring the same high precision techniques to disordered systems, and the methodology is available to find probability distributions of atoms, and hence dynamics of anharmonic vibrations and of diffusion. These advances are reminiscent of the contribution that protein crystallography has made to our understanding of complex biological systems, and of the impact that has had on the design of drugs by computer. Design of heterogeneous catalysts by computer remains a long term goal, but surface studies are on the move in that direction.     

A multiobjective approach to design chemical plants with robust dynamic operability characteristics

In chemical plants, operability problems arise mainly due to poor process designs, inaccurate models and/or the control system designs that are unable to cope with process uncertainties. In this paper, a process design methodology is presented that addresses the issue of improving dynamic operability in the present of process uncertainty through appropriate design modifications. The multiobjective nature of the design problem is carefully exploited in the subsequent formulations and a nonlinear programming approach is taken for the simultaneous treatment of both steady-state and dynamic constraints.

Scope—Today, a chemical engineer faces the challenge of designing chemical plants that can operate safely, smoothly and profitably within a dynamic process environment. For a typical chemical plant, major contributions to such an environment originate from external disturbances such as variations in the feedstock quality, different product specifications and/or internal disturbances like catalyst poisoning and heat-exchanger fouling. To guarantee a flexible operation despite such upsets, traditionally, the procedure was either to oversize the equipment or to place large storage tanks between the processing units. Proposed design methods attempted to find optimal operating regimes for chemical plants while compensating for process uncertainty through empirical overdesign factors.

Studies concerned with the interplay between the process design and operation aspects have appeared recently [1, 2] and focused on achieving better controllability upon modifying the plant design, without explicitly considering process uncertainty. Nevertheless, maintaining satisfactory dynamic operability in an environment of uncertainty remained as a pressing issue and the need was raised quite frequently for a rigorous treatment of the topic [3].

The development of new analytical tools [4, 5] made it possible to consider dynamic operability at the process design stage and modify the plant design accordingly. In this paper, a methodology is presented, that systematically guides the designer towards process designs with better dynamic operability and economics, The problem is formulated within a multiobjective optimization framework and makes extensive use of singular-value decomposition and nonlinear semi-infinite programming techniques.

Conclusions and Significance—A multiobjective optimization problem is proposed for designing chemical processes with better dynamic operability characteristics. Robustness indices are used as the indicators of dynamic operability and placed as constraints within the optimization scheme. A semi-infinite nonlinear programming problem results due to the frequency-dependent nature of such constraints. A discretization procedure is suggested to handle the infinite number of constraints and an ellipsoid algorithm allows an interactive solution of the process design problem. A process consisting of three CSTRs is treated as an example, illustrating the potential of the methodology in solving design-related operability problems.     

Synthesis of industrial systems based on value analysis

In this contribution, we present a novel methodology for flexible design of industrial systems based on their detailed differential value analysis. Evolving from graph theory, this methodology devises a mechanism for systematic structural decomposition of large-scale industrial systems into basic processing elements (paths and trees), combination of elements into subsystems and evaluation of individual elements/subsystems to correlate with the overall system margin. This helps to reduce the size of the large combinatorial problems and comprehensively analyse the multiple objectives and the sets of optimal operating states, capital investments and marginal contributions at elemental/subsystems levels that are critical for flexible designs. The approach generates the whole set of optimal solutions compared to the one point solution of the deterministic approaches (MINLP) while allowing additional complexity of process level models in the site-wide integration due to the systematic structural decomposition of a system into its basic elements/subsystems. A recent industrial application on oil upgrading system design is used to illustrate the methodology.     

A systematic methodology for the environomic design and synthesis of energy systems combining process integration,Life Cycle Assessment and industrial ecology

This paper presents a systematic methodology for sustainable process systems design, combining the principles of industrial ecology, process design and process integration, Life Cycle Assessment (LCA) and multi-objective optimization (MOO). The superstructure considers an extended decision perimeter and embeds models based either on flowsheeting software or average market technologies, for which energy and material flows are extracted from the Life Cycle Inventory (LCI) database. Therefore, the overall supply chain can be synthesized within a given action system and the systematic recyclings identified. The methodology can be used to design eco-industrial parks or urban systems, to identify the best conversion pathways of resources or waste, or to fix the optimal value of environmental taxes. It is illustrated by an application to the environomic design of an urban energy system. This case study considers multiple energy services to be supplied and waste to be treated, with their seasonal variations, indigenous and imported resources, as well as different candidate conversion technologies. Results demonstrate that integrating an environmental objective in the design procedure leads to consider different system configurations than if only economic aspects are considered. The problematic of the optimal value of a CO₂ tax is as well addressed.     

Model-based reasoning in diagnostic expert systems for chemical process plants

A prototype expert system, called MODEX, for locating the cause(s) of a set of abnormalities in a chemical process id described. We discuss a methodology that aids the developement of expert systems which are process-independent, transparent in their reasoning, and capable of diagnosing a wide diversity of faults. The domain knowlege of the system is based on qualitative reasoning principles and captures physical interconnections between equipment units as well as causal relationships among process state variables. The inference strategy uses model-based reasoning for analyzing the plant behavior. Using a variant of the technique adopted from fault tree synthesis, an initially observed abnormal symptom is considered to be a top level event and a tree structure is constructed as the system searches for a basic event to which the fault can be traced. The diagnostic reasoning process is driven by a problem reduction strategy. The knowledge base is process-independent, thereby enhancing the generality of the expert system. Reasoning from first-principles with the aid of causal and fault models facilitates the diagnoses of novel or unanticipated faults. The system does not assume a single causal origin for all initially observed faults in the chemical process. Moreover, the system has the ability to locate multiple basic causes of a fault. The methodology also permits one to investigate the causal origins of multiple, unrelated, faults. The system provides explanations to user queries at various degrees of detail. Two test cases are discussed in detail.     

Quantitative Risk Assessment for Process Design Modification and Maintenance Optimization in Refineries and Petrochemical Plants

Quantitative risk assessment is methodology based on calculating probabilities and frequencies of sequential events using Boolean algebra, and it is normally used to perform safety assessments for complex interacting systems. Although quantitative risk assessment has been commonly used in aerospace and nuclear industries, it can also be used for quantifying economic risk and for estimating possibilities of potential production losses in a petrochemical or a manufacturing plant. In developing quantitative risk assessment models for petrochemical plants, component failures as well as human (operator) errors are taken into consideration in developing the plant's fault‐tree logic, in which is used to predict probabilities of future plant upsets. This paper shows how the quantitative risk assessment can be used to rank the economic importance of the production units in a refinery for prioritizing maintenance activities. In addition, two case studies are compared to demonstrate how a quantitative risk assessment model can be used as an invaluable tool in process design optimization. The quantitative risk assessment methodology developed in this work relates production losses to the performance of the major components and the process design. This application of the quantitative risk assessment provides a basis for the risk‐informed decision‐making and optimizing allocation of plant resources in support of plant operation and maintenance activities.     

MSF Desalination plants. Availability,reliability and safety analysis

While good MSF desalination plant performance exerts a positive influence on plant economics, as part of water supply systems in isolated regions, the availability of the desalination system becomes a major design criteria. Reliability problems have historically been a major cause of poor performance for desalination plants. The emphasis of this paper is on the importance of availability modeling methodology to MSF desalination plant reliability problems by providing a context in which the effect of unit unavailability can be quantified.An assessment is made of failures and outages which impact the availability of MSF desalination plants. Limited fault tree logic for system failures is developed and reliability data from the literature is incorporated, where possible. The impact of other water supply system failures on the reliability requirements of the desalination plant is quantified as are the effects of increased average water system demands.The single largest influence on the effective capacity of a water supply system based on MSF desalination of sea water is found to be the availability and maintainability of the desalination plant. Forced outages as a result of equipment failure are significant, but other dominant contributions to unit unavailability include externally caused problems, such as silting. The design configuration of the desalination plant is also found to have an impact on the acceptability of water supply system performance.     

Hidden Markov model based approach for diagnosing cause of alarm signals

When a fault occurs in a process, it slowly propagates within the system and affects the measurements triggering a sequence of alarms in the control room. The operators are required to diagnose the cause of alarms and take necessary corrective measures. The idea of representing the alarm sequence as the fault propagation path and using the propagation path to diagnose the fault is explored. A diagnoser based on hidden Markov model is built to identify the cause of the alarm signals. The proposed approach is applied to an industrial case study: Tennessee Eastman process. The results show that the proposed approach is successful in determining the probable cause of alarms generated with high accuracy. The model was able to identify the cause accurately, even when tested with short alarm sub-sequences. This allows for early identification of faults, providing more time to the operator to restore the system to normal operation.     

Fault detection and isolation in hybrid process systems using a combined data‐driven and observer‐design methodology

A combined data‐driven and observer‐design methodology for fault detection and isolation (FDI) in hybrid process systems with switching operating modes is proposed. The main contribution is to construct a unified framework for FDI by integrating Gaussian mixture models (GMM), subspace model identification (SMI), and results from unknown input observer (UIO) theory. Initially, a GMM is built to identify and describe the multimodality of hybrid systems using the recorded input/output process data. A state‐space model is then obtained for each specific operating mode based on SMI if the system matrices are unknown. An UIO is designed to estimate the system states robustly, based on which the fault detection is laid out through a multivariate analysis of the residuals. Finally, by designing a set of unknown input matrices for specific fault scenarios, fault isolation is performed through the disturbance‐decoupling principle from the UIO theory. A significant benefit of the developed framework is to overcome some of the limitations associated with individual model‐based and data‐based approaches in dealing with the problem of FDI in hybrid systems. Finally, the validity and effectiveness of the proposed monitoring framework are demonstrated using a numerical example, a simulated continuous stirred tank heater process, and the Tennessee Eastman benchmark process. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2805–2814, 2014