An adaptive neural network algorithm for assessment and improvement of job satisfaction with respect to HSE and ergonomics program: The case of a gas refinery

Researchers have been continuously trying to improve human performance with respect to Health, Safety and Environment (HSE) and ergonomics (hence HSEE). This study proposes an adaptive neural network (ANN) algorithm for measuring and improving job satisfaction among operators with respect to HSEE in a gas refinery. To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators. The average results for each category of HSEE are used as inputs and job satisfaction is used as output for the ANN algorithm. Moreover, ANN is used to rank operators performance with respect to HSEE and job satisfaction. Finally, Normal probability technique is used to identify outlier operators. Moreover, operators with inadequate job satisfaction with respect to HSEE are identified. This would help managers to see if operators are satisfied with their jobs in the context of HSEE. This is the first study that introduces an integrated ANN algorithm for assessment and improvement of human job satisfaction with respect to HSEE program in complex systems.     

A neuro-fuzzy algorithm for assessment of health,safety, environment and ergonomics in a large petrochemical plant

Process plants such as petrochemical units have been continuously trying to improve Health, Safety, Environment and Ergonomics (HSEE) programs. This study proposes an adaptive network-based fuzzy inference system (ANFIS) for assessment of HSEE programs in a petrochemical plant. The proposed neuro-fuzzy approach is applied to a set of operators in the petrochemical unit to show its applicability and superiority. To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators. The average results for each category of HSEE are used as inputs and accomplishment of HSEE programs is used as output for the algorithm. Moreover, this algorithm is used to rank operators performance with respect to HSEE. Finally, the algorithm identifies efficient operators with respect to HSEE. This is the first study that introduces an intelligence algorithm for assessment and improvement of HSEE program in a petrochemical plant.     

An intelligent algorithm for performance evaluation of job stress and HSE factors in petrochemical plants with noise and uncertainty

This study presents an intelligent algorithm based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and statistical methods for measuring job stress in noisy and complex petrochemical plants. Job stress is evaluated against health, safety, environment and ergonomics (HSEE) program in the integrated algorithm. The algorithm is composed of seventeen distinct steps. To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators. The average results for each category of HSEE are used as inputs and job stress is used as output for the algorithm. Moreover, operators' stress level with respect to HSEE is evaluated by the algorithm. Finally, operators with weak stress level are identified. The advantage and superiority of the intelligent algorithm are shown by error analysis in contrast with conventional regression approaches. This is the first study that introduces an integrated intelligent algorithm for assessment and improvement of job stress and HSEE in noisy, complex and uncertain environment.     

Assessment and improvement of integrated HSE and macro-ergonomics factors by fuzzy cognitive maps: The case of a large gas refinery

This study analyzes and assesses the integrated health, safety, environment (HSE) and ergonomics (HSEE) factors by fuzzy cognitive maps (FCM) approach. This is achieved through integrating ergonomic and macro-ergonomic as well as occupational health and safety arrangements in an integrated modeling for assessment of their multi-faceted impact on workers' productivity, injury rate and satisfaction. This paper uses FCM to assess the direct and indirect effects of HSEE factors on system performance indicators. The results of FCM are used to develop leading indicators useful for proactive management of productivity, injury rate, and job satisfaction. The result of a comprehensive survey of 37 experts in control rooms and maintenance activities in a large gas refinery is used to show the applicability and usefulness of FCM approach. Moreover, FCM results are used to determine the causal structure of HSEE factors and system performance indicators. It is concluded that macro-ergonomics factors such as instructions and education, familiarity with organization's rules, and proper communications most contribute to improve workers' safety, satisfaction, and productivity.     

A unique intelligent approach for forecasting project completion time in oil refineries

This study presents a hybrid approach for accurate forecasting of project completion time with noisy and uncertain safety factors in oil refineries. The hybrid approach is based on artificial neural network (ANN), fuzzy mathematical programming (FMP) and conventional regression. Three indictors, namely, number of occupational injuries, number of employees and ratio of maximum useful hours over useful hour per month are considered as inputs. Also, project completion time is considered as the main output. To achieve the objective of this study, five sets of data with respect to oil refinery construction projects in various cities of Iran are collected and analyzed through statistical methods. It is shown that for the actual case of this study, ANN presents lowest mean absolute percentage error (MAPE). Also, analysis of variance (ANOVA) is used to verify and validate the results of this study. This is the first study that presents a hybrid approach for accurate estimation and forecasting of project completion time with complex, noisy and uncertain occupational factors.     

Continuous performance assessment and improvement of integrated HSE and maintenance systems by multivariate analysis in gas transmission units

In this study, an integrated “Health, Safety, and Environment (HSE)” and maintenance systems are presented. Multivariate analysis is used for continuous performance assessment and improvement of these systems. The two subjects of “Maintenance System” and “HSE” have been individually investigated several times in different studies. However, few studies have been done to integrate these two systems and provide an integrated system for their implementation. This study evaluates current maintenance and HSE systems of a Gas Transmission Unit by Data Envelopment Analysis (DEA) and Principal Component Analysis (PCA). Moreover, decision making units (DMUs) are examined and ranked. Employees are considered as DMUs. This is done through investigating and measuring their efficiencies and identifying the inefficient and less-efficient units. Since an increase in the number of inputs does not lead to an increase in the number of outputs with the same scale, an output-oriented DEA with a Variable Returns to Scale (VRS) is used. The Fuzzy DEA (FDEA) is also used in this research to decrease uncertainty existing in qualitative indicators and human error. Finally, suggestions are given to improve those DMUs. The managers and employees of the gas transmission unit constitute the statistical population of the study. To achieve the objectives of this study, standard questionnaires with respect to HSE and Maintenance system are completed by operators. The proposed approach would help policy makers and top managers of Gas Transmission Company to have a more comprehensive and thorough understanding the working conditions with respect to the maintenance and HSE features.     

基于粗糙集——粒子群神经网络的建设项目安全预测研究

回顾施工项目安全管理和安全管理研究现状,建立建设项目安全管理指标体系。利用人工神经网络非线性函数逼近能力,对项目风险因素程度预测。针对该网络当数据量大时,其结构复杂、收敛慢,易陷入局部最优的缺点,引入粗糙集对影响建设项目安全目标的不确定性因素进行约简,找出最小不确定性风险因素集,大大简化网络输入信息的表达空间维数。并结合粒子群算法收敛速度快、全局最优的寻优能力强的优点,建立基于粗糙集——粒子群神经网络的建设项目安全预测系统。通过实例验证该系统的科学性和有效性。     

Optimization of HSE in maintenance activities by integration of continuous improvement cycle and fuzzy multivariate approach: A gas refinery

Gas refineries have been continuously focusing on Health, Safety and Environment programs to improve maintenance activities. Several researches have studied on this area with different analysis methods. This study presents an integrated approach for optimization of factors contributing to the implementation of Health, Safety and Environment (HSE) in maintenance activities. HSE managers in each sector answered standard questionnaire whit respect to HES. The methodology is based on fuzzy data envelopment analysis (FDEA) and Deming's continuous improvement cycle. Also, this method is used to rank the relevant performance efficiencies in certain and uncertain conditions of each HSE sectors whit considering HSE in maintenance activities. It corresponds and integrates its registered HSE-MS with OHSAS 18001:2007 and ISO 14001:2004 to evaluate multiple inputs and outputs of over 36 subsidiary HSE divisions with parallel mission and objectives simultaneously. Also, it determines efficient target indices and could assure continuous improvement in the organization. This is the first study that introduces an integrated approach to improve HSE management programs in a gas refinery by a robust and continuous improvement approach.     

A new approach for layout optimization in maintenance workshops with safety factors: The case of a gas transmission unit

In this study, an Integrated Simulation-Data Envelopment Analysis (DEA) approach is presented for optimum facility layout of maintenance workshop in a gas transmission unit. The process of repair of incoming parts includes various operations on different facilities. The layout problem in this system involves determining the optimum location of all maintenance shop facilities. Layout optimization plays a crucial role in this type of problems in terms of increasing the efficiency of main production line. Standard types of layouts including U, S, W, Z and straight lines are considered. First, the maintenance workshop is modeled with discrete-event-simulation. Time in system, average waiting time, average machine utilization, average availability of facilities, average queue length of facilities (AL) and average operator utilization are obtained from simulation as key performance indicators (KPIs) of DEA. Also, safety index and number of operators are considered as other KPIs. Finally, a unified non-radial Data Envelopment Analysis (DEA) is presented with respect to the stated KPIs to rank all layouts alternatives and to identify the best configuration. Principle Component Analysis (PCA) is used to validate and verify the results. Previous studies do not consider safety factor in layout design problems. This is the first study that presents an integrated approach for identification of optimum layout in a maintenance workshop of gas transmission unit by incorporating safety and conventional factors.     

基于机会约束规划的航班应急调度问题研究

为应对民航突发情况,保障民航运行安全,提出应急调度这一概念。阐述常规情况下航班调度基本模型,分析其在应急情况下的弊端。引入机会约束,构建应对突发状况的应急调度模型。研究兼顾航空公司成本、航班运行安全及旅客随机需求的机型分配问题(FAP)模型和机组排班问题(CSP)模型。比较混合智能算法、隐枚举法、等价转化法的优缺点及适用度。根据案例数据,使用Matlab软件编程,并采用随机模拟与粒子群(PSO)算法相结合的智能算法对模型求解。结果表明,机会约束规划模型在考虑随机因素的情况下,比基本模型更符合实际动态环境。     

基于视线追踪技术的工艺操作人员人为失误识别研究

为了对生产加工过程中工艺操作人员的不安全行为进行有效监控，及时避免因操作失误带来的工艺安全事故，开展基于视线追踪技术的工艺操作人员人为失误识别研究。通过设计眼动实验方案，利用视线追踪技术采集操作者在工艺流程控制过程中各类失误模式的眼动数据,并对采集的数据进行统计，提取失误行为的眼动特征，并建立人为失误智能识别方法，利用距离函数聚类实现现场操作人员失误状态的准确识别。研究结果表明:以被试样本在不同区域的视线停留时间百分比作为特征参数，并运用欧氏距离函数分类法判别人员操作状态，对操作人员的不安全行为能够准确识别。     

Modeling and optimizing efficiency gap between managers and operators in integrated resilient systems: The case of a petrochemical plant

The reliability issue in complex industrial systems such as oil, gas, petrochemical companies, nuclear and aviation industries has been of great importance. Resilience engineering (RE) is a new attitude toward the improvement of safety and reliability in the stated systems. One of the challenges a resilient system might face is the gap between work as imagined by managers and work as actually done by operators. This study will introduce a new framework named integrated resilience engineering (IRE) as a result of developing the concept of RE. The data used in this research have been obtained by means of questionnaire from a petrochemical company. Thereafter, the efficiency of operators and managers are calculated in RE and IRE frameworks through data envelopment analysis (DEA) approach. Then, the gaps between managers and operators are analyzed in two frameworks. The results are indicative of a significant growth in the number of efficient operators and managers in IRE framework compared to RE framework. Besides, the efficiency mean of managers and operators in IRE framework has experienced the growth of 1.8% and 5% compared to RE framework, respectively. The efficiency gap between managers and operators in IRE framework has also enjoyed the improvement of 88% compared to RE framework. Generally, it can be said that the suggested items of this research has led to the betterment of managers and operators’ efficiency and of the efficiency gap between them. Therefore, these items can improve the resilience and safety of complex systems. The results of Spearman test show that there is a strong direct correlation between the DEA results in two frameworks. This is the first study that examines the efficiency gap between operators and managers based on the RE principles and by means of DEA approach.     

Environmental emergency decision support system based on Artificial Neural Network

We present a general methodology for developing environmental emergency decision support systems (EEDSS) based on an Artificial Neural Network (ANN). We highlight the method for developing the system using an illustrative example of an unexpected atmospheric accident with an ANN prototype system for a district in Shanghai. The network architecture of the ANN is introduced. Then the development process and key technologies are addressed. The procedures for matching the environmental emergency decision support characteristics are as follows: (1) digitization (coding) of case information and emergency measures, in which the information of cases are divided into the input attributes and decision-making information, and standardized and digitized through the Feature Evaluation (FE) method and the Intensity Hierarchical (IH) method, respectively; (2) construction of environmental emergency ANN, in which Gradient Descent with Momentum and Adaptive Learning Rate (GDMALR) method (traingdx function), a modified back-propagation algorithm, is employed to do training; and (3) translation (decoding) of decision-making information, in which output data of ANN is interpreted into practical contingency measures with Translation Based on Conventional Import Ratios (TBCIR) method. The training features, time, errors, accuracy, and input attribute weights of the prototype system are analyzed. The usage of the prototype system is demonstrated through a hypothetical case. This article encounters the challenge of ANN’s own lack of training samples. We discuss to the concept of integrating Case-Based Reasoning (CBR), Genetic Algorithm (GA), and ANN to overcome this difficulty and form a technology system for generating useful decision support information for environmental emergency response.     

基于CREAM的高温熔融金属作业人员可靠性分析

为了对冶金企业高温熔融金属作业人员的可靠性进行评估和量化,基于认知可靠性与失误分析方法(CREAM),提出了适用于高温熔融金属作业的人员可靠性分析方法。该方法给出了通用效能条件(Common Performance Condition, CPC)的评估细则,采用层次分析法(Analytic Hierarchy Process,AHP)与熵值法相结合的CPC因子权重确定方法,利用模糊数学原理实现CPC因子输入的模糊化,从而计算高温熔融金属作业人员失误概率(HEP)和可靠度。通过对炼钢冶炼作业的人员可靠性分析的结果表明:该方法合理有效,具有一定的应用性。     

Predicting of blasting vibrations in Sarcheshmeh copper mine by neural network

Artificial Neural Networks (ANN) have proven to be an effective tool for solving complex engineering problems requiring estimation, pattern recognition, and classification of variables. Ground vibration caused by blasting imposes damages and financial penalties and as such must be predicted accurately. In this study, the potentials of ANN are investigated in prediction of ground vibrations due to blasting in open pit mines. Real vibration data is recorded using PDAS 100 seismometers, and used as input data for ANN. Using back propagation algorithm and performance function, root mean square error (RMSE) the network, containing four hidden layers and 23 data sets, was trained. Six sets of data were used to make sure that correct training had been carried out. This produced the coefficient correlation of 0.99355.     

多因素耦合条件下硫化矿自燃神经网络动态预测模型研究

硫化矿石自燃是多种因素、多场耦合综合作用的结果,是一典型的非线性问题。笔者应用人工神经网络技术,以Matlab软件为平台,通过现场调查和理论分析,建立了矿石含硫量、通风强度、环境温度3因素与硫化矿石自燃之间的预测模型;通过数据样本学习与部分现场监测数据相结合进行模拟,研究表明预测数据与实测结果基本吻合,误差控制在10%以内,取得了较好的效果。该研究为预防硫化矿石自燃提供一个新的思路和方法,具有一定的理论意义和应用价值。     

基于AHP与重要性指标筛选的神经网络评价模型与应用

以对评价目标有影响的所有评价指标作为神经网络的输入,会导致网络模型复杂、降低其性能和影响计算精度的问题,因而提出基于层次分析法和重要性指标筛选法的神经网络评价建模方法即首先运用层次分析法对评价指标进行重要度排序,然后利用重要性指标筛选法过滤出对评价目标有重要影响的指标,以其结果作为神经网络的输入。该法不仅简化网络模型,而且提高网络的性能和计算精度。运用该法对企业安全工作评价,结果证明,不仅是可行的,而且达到了预期的目的。     

应急状态下矿井瓦斯爆炸致灾因子传播快速分类器研究*

为了在矿井瓦斯爆炸灾变发生后,快速确定瓦斯爆炸冲击波的压力、温度、有毒有害气体等致灾因子在井巷网络中的传播情况。利用CFD数值模拟或爆炸实验获得瓦斯爆炸冲击波的压力、温度、有毒有害气体等致灾因子传播大数据,将影响瓦斯爆炸传播的因素以及观测点等参数作为人工神经网络的输入节点,压力、温度等致灾因子作为输出节点,建立瓦斯爆炸致灾因子传播快速预测机器学习模型,解决CFD数值模拟的建模、计算及数据分析处理等过程耗时大、不适应灾变应急的快速响应等问题。研究结果表明:在给定爆炸位置和爆炸当量的均直巷道,获得任一点的爆炸冲击波压力、温度以及有毒有害气体所需时间是瞬时的,人工神经网络平均训练误差为6.92 %,有训练样本的验证误差为5.24 %,无训练样本的验证误差为6.88 %。     

五氯硫酚锌盐粉尘爆炸下限浓度的实验研究

五氯硫酚锌盐的一些基本的危险性参数,如燃烧爆炸性能,目前国内外报道极少。笔者采用野外定性燃烧试验、哈特曼管实验及20 L球实验,对该物质粉尘爆炸的危险性进行研究。结果表明,该物质具有燃烧爆炸危险性,但与细小片状铝粉(燃爆危险性很强烈)相比,其粉尘的燃爆危险性很弱。以硅系点火具作为点火源,在20 L爆炸球中测试获得该粉尘爆炸下限浓度约为213 g/m3。根据ISO-6184及VD I-3673等标准,认为该粉尘的爆炸猛烈度为1级。所得结果为该物质的生产及使用安全提供了重要的参考。