HSE管理体系暨环境安全专家系统在石化企业中的应用
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摘要
伴随着社会的发展,人类对资源的需求与日俱增;而与此同时,人类对自身的生存环境也越来越关心。在这种情况下,化工企业需要在生产规模的扩大与员工身体健康、装置的安全运行以及环境保护这样的矛盾中做出选择。安全、环境和健康管理体系(以下简称HSE)的出现,使较好地解决这一矛盾成为可能。
HSE管理是一种事前进行风险分析,确定其自身活动可能发生的危害和后果,从而采取有效的防范手段和控制措施防止其发生,以便减少可能引起的人员伤亡、财产损失和环境污染的有效管理方式。进入21世纪,世界各国石油公司HSE管理重视程度普遍提高,HSE管理已成为世界性的潮流与主题,建立和持续改进HSE管理体系已成为国际石油公司健康、安全与环境管理的大趋势。HSE管理体系的审核向标准化迈进,HSE管理体系与质量管理体系逐步一体化。世界各国的环境立法更加系统,环境标准更加严格。
本文在回顾了HSE的发展历程和变化后,针对当前国内石化企业在HSE管理中容易出现的一些问题,提出了将环境安全专家系统融入HSE管理体系,考察审核石化企业在HSE实施中的方针、活动,不断促进石化企业的HSE管理,把外部驱动转化为HSE的内在驱动。
本文在随后的章节中给出了系统的总体设计和架构,并分别阐述了专家系统的整体结构以及各组成部分的功能和作用。环境安全专家系统有三种可能的输入:管理员录入、传感器/DCS输入和专家输入,一种输出:针对普通用户的输出。管理员录入一些历史记录以及无法通过传感器或者DCS获得的和企业有关的信息。传感器或DCS的输入是在企业进行生产时,生产环境的动态变化情况。专家的输入是把一些专家知识通过对具体案例的判断记录到专家系统中,系统通过学习专家的判断结果从而间接获得潜在的专家知识。专家系统记录并学习管理员录入的一些历史记录以及专家对案例的判断,利用一些常用的智能模型,形成知识库。然后,对于企业生产时的传感器或DCS的输入数据,专家系统可以利用建立好的知识库和模型库,做出判断,保障安全生产的进行;或者,对于输入的关于企业生产区的各个环境指标进行判断,做出环境质量评估。这些结果反馈给普通用户,让普通用户了解当前生产的安全状况或者生产区环境质量,从而有利于管理人员发现当前企业在HSE管理体系实施运行中存在的问题,进一步改善企业的HSE管理。
本文提出的专家系统有以下主要模块:数据采集和处理、推理机、解释器、模型库、知识库和用户界面。数据采集和处理是系统按照一定的时间间隔从传感器和DCS获取数据的过程,处理工作主要有数据滤波和数据平滑。推理机由一组程序组成,它包括调度器、执行器和协调器三部分。调度器控制整个推理流程;执行器执行调度器所选定的动作,并且负责读取知识库中的知识和模型库中的模型信息;协调器的主要作用是当得到新数据或新假设时,对已得到的结果进行修正,以保持结果前后的一致性。解释器是解释推理过程的程序,它能够记录用户的提问、系统的输入、推理和求解的过程,形成专家系统的日志,便于对专家系统进行检查、维护和修正。模型库包含了五种专家系统中常用的智能模型以及针对这些智能模型的Boosting融合模型,此外,还有专用于石化企业生产区的环境质量评估模型。知识库是问题求解所需要的领域知识的集合,包括基本事实、规则和其他有关信息。最后还有系统与用户进行交流的接口,用户界面。
研制环境安全专家系统要借鉴信息科学中人工智能方面的技术,本文介绍了五个在人工智能领域中最常见的智能模型:决策树模型、神经网络模型、K近邻模型、贝叶斯模型、支持向量机模型。决策树模型是一种逼近离散值函数的方法,对噪声数据有很好的健壮性且能够学习析取表达式;神经网络模型由一系列简单的单元相互密集连接构成的,其中每一个单元有一定数量的实值输入,并产生单一的实数值输出;K近邻模型是一种基于样本的学习方法,它把学习任务推迟到分类时进行,因此有时也被称为“消极学习法”;贝叶斯模型基于如下假定:待考查的量遵循某概率分布,且可根据这些概率及已观察到的数据进行推理,做出最优的决策;支持向量机模型是建立在统计学习理论的VC维理论和结构风险最小原理基础上的,根据有限的样本信息在模型的复杂性和学习能力之间寻求最佳折衷,以期获得最好的推广能力。这些模型在解决实际问题的过程中各有千秋,在构造实际的专家系统过程中必须仔细考虑,做出合适的选择。为此,本文提出Boosting融合方法,通过迭代运算得到各方法在融合时的权重,这样在发挥了各种方法的特长的同时,又尽量规避了各自的不足。实验证明,经过融合后的系统性能超过了只采用任一单独模型的系统性能。
HSE管理体系中,对环境质量的综合评估是一项重要的工作。为此,本文先提出采用评价环境质量常用的综合指数法评价生产区环境质量,建立了生产区环境质量评价的指标体系,为后面的模糊综合评价法打下了基础。综合指数法的关键问题是权重的确定,它关系到综合指数法的精确与否。鉴于生产区环境质量评价是一个典型的多准则决策问题,本文在其后所述的基于AHP-FUZZY的生产区环境质量评估模型运用定性与定量相结合、专家评估与科学计算互为补充的系统分析方法,具有系统全面、科学可靠、简单实用等特点,为生产区环境质量综合评估提供了一定的理论指导。本文再结合具体的实例,分析了基于AHP-FUZZY的综合评价方法同综合指数法的差异,及形成差异的原因;显示了基于AHP-FUZZY的综合评价方法的优越性。
最后,面向石化企业,本文从系统说明、功能、要求三个方面,进行需求分析。对于模型库中未在前面章节详细介绍的模型,给出了较为具体的阐述和相应的算法。同时,本文还展示了一些系统的运行状态。
As the development of the society, the needs for the resources are increasing. Meanwhile, the care for the living condition is getting more and more. In such a situation, chemical corporations have to make choices between enlarging the production scale and the protection of employees' health, the equipment and the environment. The appearance of Health, Safety and Environment Management System (HSE as short) makes it possible to solve this problem properly.
HSE management is an effective management mode, which analyzes the risks beforehand and make certain of the possible danger and sequent from its activities. So it takes effective protections and controls to prevent the happening of the possible danger and sequent in order to reduce the possible casualty of people, the loss of properties and the pollution of the environment. In the 21~(st) century, the oil companies all over the world have higher regards for the HSE management which has become the cosmopolitan trend and theme. The set up and the persistent improvement of the HSE management system have become the main direction of the management of the health, the safety and the environment in international oil companies. The auditing the HSE management system is standardizing. And the HSE management system is incorporating the quality management system. The environmental legislation in the world is more systemic and the environmental standard is stricter.
After reviewing the development and changes of the HSE, according to some problems easily aroused from the current HSE management of the domestic chemical corporations, the environment safety expert system is proposed to be fused into the HSE management system, which can review and look through the policies and the activities during the implement of the HSE of the chemical corporation. That transforms the outer drive into the internal one, so it can keep on improving the HSE management of the chemical corporations.
The general design and the architecture of the system are given in the following chapter, in which we also describe the whole structure of the expert system and the functionality and the effect of each component. The environment safety expert system has three possible inputs, which are the input of the administrator, the input of the sensors and the DCS, and the input of the experts, and one output, which is the output for the common users. The administrator input some historical records and some information related to the corporation that can't be gained by the sensors or the DCS. The input of the sensors and the DCS is the dynamic change of the productive environment during the production. The input of the experts records the judgments of special cases by the experts, in the way of which the system gains the potential expert knowledge indirectly. The expert system records and learns from some historical records input by the administrator and the judgments of cases by the experts through some general intelligent models, and forms the knowledge library. Then, to the input data from the sensors or the DCS during the corporation production, the expert system can make a judgment with the built knowledge library and the model library in order to keep the production safe. Or else, the input environmental indexes of the corporation productive areas are judged and the environmental quality is estimated. These results are fed back to the common users to let them realize the current safety status of the production or the quality of the productive areas. That benefits the managers to discover the current problems of the corporation during the implementation of the HSE management system and improve the HSE management of the corporation further.
The proposed expert system has the following main modules: the collection and the procession of data, the inference engine, the interpreter, the model library, the knowledge library, and the user interface. The collection and the procession of data is the procedure that the system gets the data from the sensors and the DCS at some interval. The procession work mainly includes the data filtering and the data smoothing. The inference engine consists of a set of programs, which are the scheduler, the executor, and the coordinator. The scheduler controls the whole flow of the inference. The executor executes the action selected by the scheduler and takes charge of reading the knowledge from the knowledge library and the model information from the model library. The main function of the coordinator is to modify the gained result in order to keep the consistence of the results after getting new data or new hypothesis. The interpreter is the program which interprets the procedure of the inference. It can record the questions from the users, and the inputs, the inference and the solution procedure of the system to construct the log of the expert system, which is convenient for the check, the maintenance, and the fix of the expert system. The model library includes five intelligent models commonly used in the expert system and the Boosting fusion model aiming at these intelligent models. What's more, there are estimation models of the environment quality on the petrochemical corporation productive areas. The knowledge library is the set of the domain knowledge that can settle the problems, including the basic facts, rules, and some other related information. At last there is the inferface for the communication between the system and the users, the user interface.
Developing the expert system on the environment safety makes use of the artificial intelligence techniques in the information science for references. The thesis summarizes five common intelligent methods in the field of artificial intelligence: the decision tree model, the neural networks model, the K-nearest neighbor model, the Bayesian model, and the Support Vector Machine model. The decision tree model is a method that approximates the discrete function. It's robust to the noisy data and able to learn the disjunction expression. The neural networks consist of a series of simple densely inter-connected unit, which has a mount of real value inputs and produces a single real value output. The K-nearest neighbor model is a learning model based on the samples. It puts off the learning task to the time of the classification. That's why sometimes it's called 'the lazy learning method'. The Bayesian model is based on the following assumption: the variant to be examined follows some probability distribution, and we can make the inference and the optimal decision according to these probabilities and the observed data. The Support Vector Machine model is built on the VC theory of the statistical learning theory and the base of the minimum of the structural risk. It seeks the best trade off between the complexity of the model and the learning ability according to the limited sample information in order to obtain the best generality. These methods have advantages and disadvantages of their own while solving problems. A good consideration and a proper choice have to be taken when developing the expert system on the environment safety. For this sake, the proposed Boosting fusion algorithm gains the weights of each method during the fusion through iterative computations. It exerts the advantages of these methods while avoiding their disadvantages. The experiment shows that the performance of the fused system is better than any other system adopting only the single model.
In the HSE management system, the evaluation of the environment quality is an important work. So a common integrative index method is first proposed to evaluate the environment quality of the production quarter. An index system for the production quarter is set up, which is the basis of the fuzzy integrative evaluation method. The key issue of the integrated index method is the fixing of the weights, which affects the accuracy of the integrated index method. The evaluation of the production quarter is a typical multiple rule decision problem. An evaluation model based on AHP-FUZZY incorporates the qualitative and the quantitative analysis, the evaluation of experts and scientific computation. With the instances, differences between the method based on AHP-FUZZY and the integrated index method are analyzed, which shows the advantages of the evaluation method based on AHP-FUZZY.
At last, facing the chemical corporation, the demand is analyzed from three aspects, which are the explanation, the functionality, and the requirement of the system. The detailed description and the corresponding algorithm of the models, which haven't been introduced particularly in the former chapters, are given. Meanwhile, some running dynamics of the system is shown.
HSE管理是一种事前进行风险分析,确定其自身活动可能发生的危害和后果,从而采取有效的防范手段和控制措施防止其发生,以便减少可能引起的人员伤亡、财产损失和环境污染的有效管理方式。进入21世纪,世界各国石油公司HSE管理重视程度普遍提高,HSE管理已成为世界性的潮流与主题,建立和持续改进HSE管理体系已成为国际石油公司健康、安全与环境管理的大趋势。HSE管理体系的审核向标准化迈进,HSE管理体系与质量管理体系逐步一体化。世界各国的环境立法更加系统,环境标准更加严格。
本文在回顾了HSE的发展历程和变化后,针对当前国内石化企业在HSE管理中容易出现的一些问题,提出了将环境安全专家系统融入HSE管理体系,考察审核石化企业在HSE实施中的方针、活动,不断促进石化企业的HSE管理,把外部驱动转化为HSE的内在驱动。
本文在随后的章节中给出了系统的总体设计和架构,并分别阐述了专家系统的整体结构以及各组成部分的功能和作用。环境安全专家系统有三种可能的输入:管理员录入、传感器/DCS输入和专家输入,一种输出:针对普通用户的输出。管理员录入一些历史记录以及无法通过传感器或者DCS获得的和企业有关的信息。传感器或DCS的输入是在企业进行生产时,生产环境的动态变化情况。专家的输入是把一些专家知识通过对具体案例的判断记录到专家系统中,系统通过学习专家的判断结果从而间接获得潜在的专家知识。专家系统记录并学习管理员录入的一些历史记录以及专家对案例的判断,利用一些常用的智能模型,形成知识库。然后,对于企业生产时的传感器或DCS的输入数据,专家系统可以利用建立好的知识库和模型库,做出判断,保障安全生产的进行;或者,对于输入的关于企业生产区的各个环境指标进行判断,做出环境质量评估。这些结果反馈给普通用户,让普通用户了解当前生产的安全状况或者生产区环境质量,从而有利于管理人员发现当前企业在HSE管理体系实施运行中存在的问题,进一步改善企业的HSE管理。
本文提出的专家系统有以下主要模块:数据采集和处理、推理机、解释器、模型库、知识库和用户界面。数据采集和处理是系统按照一定的时间间隔从传感器和DCS获取数据的过程,处理工作主要有数据滤波和数据平滑。推理机由一组程序组成,它包括调度器、执行器和协调器三部分。调度器控制整个推理流程;执行器执行调度器所选定的动作,并且负责读取知识库中的知识和模型库中的模型信息;协调器的主要作用是当得到新数据或新假设时,对已得到的结果进行修正,以保持结果前后的一致性。解释器是解释推理过程的程序,它能够记录用户的提问、系统的输入、推理和求解的过程,形成专家系统的日志,便于对专家系统进行检查、维护和修正。模型库包含了五种专家系统中常用的智能模型以及针对这些智能模型的Boosting融合模型,此外,还有专用于石化企业生产区的环境质量评估模型。知识库是问题求解所需要的领域知识的集合,包括基本事实、规则和其他有关信息。最后还有系统与用户进行交流的接口,用户界面。
研制环境安全专家系统要借鉴信息科学中人工智能方面的技术,本文介绍了五个在人工智能领域中最常见的智能模型:决策树模型、神经网络模型、K近邻模型、贝叶斯模型、支持向量机模型。决策树模型是一种逼近离散值函数的方法,对噪声数据有很好的健壮性且能够学习析取表达式;神经网络模型由一系列简单的单元相互密集连接构成的,其中每一个单元有一定数量的实值输入,并产生单一的实数值输出;K近邻模型是一种基于样本的学习方法,它把学习任务推迟到分类时进行,因此有时也被称为“消极学习法”;贝叶斯模型基于如下假定:待考查的量遵循某概率分布,且可根据这些概率及已观察到的数据进行推理,做出最优的决策;支持向量机模型是建立在统计学习理论的VC维理论和结构风险最小原理基础上的,根据有限的样本信息在模型的复杂性和学习能力之间寻求最佳折衷,以期获得最好的推广能力。这些模型在解决实际问题的过程中各有千秋,在构造实际的专家系统过程中必须仔细考虑,做出合适的选择。为此,本文提出Boosting融合方法,通过迭代运算得到各方法在融合时的权重,这样在发挥了各种方法的特长的同时,又尽量规避了各自的不足。实验证明,经过融合后的系统性能超过了只采用任一单独模型的系统性能。
HSE管理体系中,对环境质量的综合评估是一项重要的工作。为此,本文先提出采用评价环境质量常用的综合指数法评价生产区环境质量,建立了生产区环境质量评价的指标体系,为后面的模糊综合评价法打下了基础。综合指数法的关键问题是权重的确定,它关系到综合指数法的精确与否。鉴于生产区环境质量评价是一个典型的多准则决策问题,本文在其后所述的基于AHP-FUZZY的生产区环境质量评估模型运用定性与定量相结合、专家评估与科学计算互为补充的系统分析方法,具有系统全面、科学可靠、简单实用等特点,为生产区环境质量综合评估提供了一定的理论指导。本文再结合具体的实例,分析了基于AHP-FUZZY的综合评价方法同综合指数法的差异,及形成差异的原因;显示了基于AHP-FUZZY的综合评价方法的优越性。
最后,面向石化企业,本文从系统说明、功能、要求三个方面,进行需求分析。对于模型库中未在前面章节详细介绍的模型,给出了较为具体的阐述和相应的算法。同时,本文还展示了一些系统的运行状态。
As the development of the society, the needs for the resources are increasing. Meanwhile, the care for the living condition is getting more and more. In such a situation, chemical corporations have to make choices between enlarging the production scale and the protection of employees' health, the equipment and the environment. The appearance of Health, Safety and Environment Management System (HSE as short) makes it possible to solve this problem properly.
HSE management is an effective management mode, which analyzes the risks beforehand and make certain of the possible danger and sequent from its activities. So it takes effective protections and controls to prevent the happening of the possible danger and sequent in order to reduce the possible casualty of people, the loss of properties and the pollution of the environment. In the 21~(st) century, the oil companies all over the world have higher regards for the HSE management which has become the cosmopolitan trend and theme. The set up and the persistent improvement of the HSE management system have become the main direction of the management of the health, the safety and the environment in international oil companies. The auditing the HSE management system is standardizing. And the HSE management system is incorporating the quality management system. The environmental legislation in the world is more systemic and the environmental standard is stricter.
After reviewing the development and changes of the HSE, according to some problems easily aroused from the current HSE management of the domestic chemical corporations, the environment safety expert system is proposed to be fused into the HSE management system, which can review and look through the policies and the activities during the implement of the HSE of the chemical corporation. That transforms the outer drive into the internal one, so it can keep on improving the HSE management of the chemical corporations.
The general design and the architecture of the system are given in the following chapter, in which we also describe the whole structure of the expert system and the functionality and the effect of each component. The environment safety expert system has three possible inputs, which are the input of the administrator, the input of the sensors and the DCS, and the input of the experts, and one output, which is the output for the common users. The administrator input some historical records and some information related to the corporation that can't be gained by the sensors or the DCS. The input of the sensors and the DCS is the dynamic change of the productive environment during the production. The input of the experts records the judgments of special cases by the experts, in the way of which the system gains the potential expert knowledge indirectly. The expert system records and learns from some historical records input by the administrator and the judgments of cases by the experts through some general intelligent models, and forms the knowledge library. Then, to the input data from the sensors or the DCS during the corporation production, the expert system can make a judgment with the built knowledge library and the model library in order to keep the production safe. Or else, the input environmental indexes of the corporation productive areas are judged and the environmental quality is estimated. These results are fed back to the common users to let them realize the current safety status of the production or the quality of the productive areas. That benefits the managers to discover the current problems of the corporation during the implementation of the HSE management system and improve the HSE management of the corporation further.
The proposed expert system has the following main modules: the collection and the procession of data, the inference engine, the interpreter, the model library, the knowledge library, and the user interface. The collection and the procession of data is the procedure that the system gets the data from the sensors and the DCS at some interval. The procession work mainly includes the data filtering and the data smoothing. The inference engine consists of a set of programs, which are the scheduler, the executor, and the coordinator. The scheduler controls the whole flow of the inference. The executor executes the action selected by the scheduler and takes charge of reading the knowledge from the knowledge library and the model information from the model library. The main function of the coordinator is to modify the gained result in order to keep the consistence of the results after getting new data or new hypothesis. The interpreter is the program which interprets the procedure of the inference. It can record the questions from the users, and the inputs, the inference and the solution procedure of the system to construct the log of the expert system, which is convenient for the check, the maintenance, and the fix of the expert system. The model library includes five intelligent models commonly used in the expert system and the Boosting fusion model aiming at these intelligent models. What's more, there are estimation models of the environment quality on the petrochemical corporation productive areas. The knowledge library is the set of the domain knowledge that can settle the problems, including the basic facts, rules, and some other related information. At last there is the inferface for the communication between the system and the users, the user interface.
Developing the expert system on the environment safety makes use of the artificial intelligence techniques in the information science for references. The thesis summarizes five common intelligent methods in the field of artificial intelligence: the decision tree model, the neural networks model, the K-nearest neighbor model, the Bayesian model, and the Support Vector Machine model. The decision tree model is a method that approximates the discrete function. It's robust to the noisy data and able to learn the disjunction expression. The neural networks consist of a series of simple densely inter-connected unit, which has a mount of real value inputs and produces a single real value output. The K-nearest neighbor model is a learning model based on the samples. It puts off the learning task to the time of the classification. That's why sometimes it's called 'the lazy learning method'. The Bayesian model is based on the following assumption: the variant to be examined follows some probability distribution, and we can make the inference and the optimal decision according to these probabilities and the observed data. The Support Vector Machine model is built on the VC theory of the statistical learning theory and the base of the minimum of the structural risk. It seeks the best trade off between the complexity of the model and the learning ability according to the limited sample information in order to obtain the best generality. These methods have advantages and disadvantages of their own while solving problems. A good consideration and a proper choice have to be taken when developing the expert system on the environment safety. For this sake, the proposed Boosting fusion algorithm gains the weights of each method during the fusion through iterative computations. It exerts the advantages of these methods while avoiding their disadvantages. The experiment shows that the performance of the fused system is better than any other system adopting only the single model.
In the HSE management system, the evaluation of the environment quality is an important work. So a common integrative index method is first proposed to evaluate the environment quality of the production quarter. An index system for the production quarter is set up, which is the basis of the fuzzy integrative evaluation method. The key issue of the integrated index method is the fixing of the weights, which affects the accuracy of the integrated index method. The evaluation of the production quarter is a typical multiple rule decision problem. An evaluation model based on AHP-FUZZY incorporates the qualitative and the quantitative analysis, the evaluation of experts and scientific computation. With the instances, differences between the method based on AHP-FUZZY and the integrated index method are analyzed, which shows the advantages of the evaluation method based on AHP-FUZZY.
At last, facing the chemical corporation, the demand is analyzed from three aspects, which are the explanation, the functionality, and the requirement of the system. The detailed description and the corresponding algorithm of the models, which haven't been introduced particularly in the former chapters, are given. Meanwhile, some running dynamics of the system is shown.
引文
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