Safety Of Industrial Processes Research Articles

Novel adaptive fault detection method based on kernel entropy component analysis integrating moving window of dissimilarity for nonlinear dynamic processes

Fault detection of nonlinear dynamic processes can ensure the safety of industrial production processes. Industrial process data are mostly autocorrelated along with strong nonlinear characteristics. And these significant characteristics interact with each other and limit the fault detection performance of traditional methods. Therefore, this paper presents a novel adaptive fault detection method for nonlinear dynamic processes based on kernel entropy component analysis (KECA) integrating the moving window of dissimilarity (DMW) (KECA-DMW). The KECA is used to map the raw data and capture the nonlinear features of the data, which combine with moving window techniques to build the fault detection model. In the process of updating the data in the moving window, the data information of the historical window is combined with that of the current window to obtain a more comprehensive judgment of the current moment. Then a dynamic update fusion method with adaptive weight allocation based on the dissimilarity index is proposed by analyzing the data characteristics of window information at different moments through the dissimilarity. Finally, three example studies with a numerical example, a closed-loop continuously stirred tank reactor and a Tennessee-Eastman process are used to validate the effectiveness of the proposed method. Compared with other nonlinear dynamic process fault detection methods, the results verify the effectiveness of the proposed method in the process monitoring performance of nonlinear dynamic processes in terms of false alarm rate and fault detection rate, where the false alarm rates of the proposed method are only 2%, 1.83%, and 4.33%, while the fault detection rates are 97.4%, 96.83%, and 86.25%, respectively.

Flash point of binary and ternary monoterpene mixtures: Experimental and modeling

The flash point (FP) of pure or mixtures of flammable substances is an important indicator in designing fire risk mitigation and prevention measures in the chemical and oil industry. Although FP data of fuel constituents and blends are often found in the literature, studies addressing other flammable mixtures, such as essential oils (EOs) and their constituents (terpenes and terpenoids), are scarce. EOs are aromatic, volatile liquid mixtures extracted from plant matrices that present diverse biological properties and find numerous applications in the food, pharmaceutical, cosmetic, and fragrance industries. In this work, experimental FP measurements of binary and ternary mixtures of four structurally diverse monoterpenes (carvone, eucalyptol, limonene, and linalool), widely found in different EO profiles, were carried out. Besides, the Liaw-UNIFAC model and the COSMO-RS model were used to calculate the FP data of the studied mixtures, resulting in global root-mean-square deviations (RMSD) of 2.0 K and 0.7 K, respectively. Both models deliver better predictions than the ideal approach (RMSD = 2.4 K) for the studied systems, demonstrating the importance of considering nonideal effects when estimating the FP data of terpene mixtures. These results provide essential information for accurate process safety and fire risk assessment in the EO industry.

Parallel-connected timers and deadbands for reducing the number of false and missed alarms

False and missed alarms are detrimental to the safety of industrial processes. In practice, false and missed alarms are often reduced by timers and deadbands. An approach is hereby developed to simultaneously design timers and deadbands to be connected in parallel. Most of conventional approaches, in contrast, are on the separate design of timers or deadbands. Optimal parameters of timers and deadbands to achieve user-defined values of false and missed alarm ratios are calculated by solving an optimization problem. Conventional approaches depend on statistics of analog variables strictly presumed to be identically and independently distributed (IID). However, the developed approach depends on cumulative distributions of time durations and amplitude deviations, so that it requires no IID premise. The reliable joint cumulative distribution functions of time durations and amplitude deviations are estimated via the Bayesian method and inverse coefficients of variation. The validity of the developed approach is demonstrated by numerical and industrial examples.

Ash deposition characteristics of flue gas across a tube using the Eulerian multiphase model with dynamic mesh technique

The ash deposition problem majorly affects the stability, safety, and economy of industrial production processes. To investigate the transient ash deposition characteristics of the flue gas flowing across a tube, the Eulerian multiphase model combined with a dynamic mesh technique is first used in this work. The deposition and the removal processes are both taken into account. Four deposition mechanisms are considered in the deposition process, including Brownian and eddy diffusion, gravity, thermophoretic, and turbophoretic forces. Based on the experimental verification results, it is found that the Eulerian multiphase model combined with a dynamic mesh technique can more accurately predict the ash deposition behavior. The overall fouling characteristics are analyzed by the changes in net deposition mass and deposition morphology under seven different working conditions. The results showed that the ash deposits are mainly formed in two regions: β = 165° ∼ 195° and β = 305° ∼ 55°. In addition, one peak is observed in the deposition region on the windward side, and three peaks exist in the deposition region on the leeward side. The periodic shedding regularity of vortices, the separation process of the boundary layer, and the flow patterns of each feature point on the tube surface are studied to investigate the influence of the flow field characteristics on the deposition characteristics. In addition, the causes of the ash deposition in various locations are discussed in detail from the perspective of the deposition mechanism. The findings of this study are beneficial for understanding the ash deposition process and provide an improved approach for ash deposition simulation.

A weighted k-nearest neighbor process monitoring method based on statistical volume pattern analysis

Process monitoring technology has developed rapidly in response to the increasing demand for safer and more reliable systems in modern process operations. Online process monitoring plays an important role not only in ensuring process safety through the timely detection of process faults but also in improving process productivity and product quality. One of the outstanding features of modern manufacturing facilities, which are large in scale and highly complex, is that the processes contain numerous variables operating under closed-loop control. Fully exploiting and utilizing the valuable information in these variables will benefit early and accurate fault detection and diagnosis of processes, minimizing downtime, increasing plant operational safety, and reducing manufacturing costs. The development of process monitoring technology is of great importance to ensure the operational safety, reliability, and economy of complex industrial processes. With the continuous development of industrial processes, the collection and use of process data are gradually increasing, and data-driven multivariate statistical process monitoring methods have developed significantly. A weighted k-nearest neighbor process monitoring method based on SPA is proposed for the problem of multimodal properties of process statistics. When the covariance difference between different modes is large, the multimodal characteristics of the original variable space are retained in the statistic feature space. By introducing weights and assigning weights to the distances between statistic samples, the proposed method can regulate the distances between statistic samples in modes with larger covariance and those with smaller covariance to the same scale, overcoming the limitations of the SPA-based process monitoring method in multimodal fault detection.

Monitoring Method of Non-Gaussian Process Based on Fractal Analysis With Kernel Independent Component Regression

Process monitoring is very important for the safety of industrial production processes. The traditional monitoring method based on independent component analysis has the disadvantages as follows. (1) The importance ordering problem of independent components has not been solved; (2) The dynamic problem is not considered. To address these issues, a fractal dimension-based dynamic kernel independent components regression (FD-KICR) method is proposed. The contributions of the proposed method are as follows: (1) The intrinsic dimension of the data is calculated through the improved fractal dimension, and then the number of selected nonlinear ICs (nICs) is determined; (2) The time lags are computed with fractal dimension to effectively describe the dynamic structure of data; (3) By correlating temperature with independent components selection and indirectly monitoring temperature changes through bands of independent components, this method can effectively monitor the safety of the production process. This proposed method is applied to the electrical fused magnesia furnace (EFMF). The experience results show the effectiveness of this method.

Effects of six sigma implementation on occupational health and safety in industrial diesel engine production and maintenance processes

This study researched the results of implementing the Six Sigma approach to occupational health and safety in the Diesel engine industry in Europe. Six Sigma is a robust philosophy used in manufacturing industries to improve production processes, reduce risks, and enhance quality. It also aims to prevent work accidents and create a safer working environment by eliminating process faults. Employers must prioritize employee protection and a healthy working environment, and the Six Sigma methodology offers opportunities to achieve this. The study focused on the accident and injury types and amounts of one of the industrial Diesel engine production and maintenance companies operating worldwide. Occupational accident statistics were used between 2019-2022 as data. The path taken in occupational health and safety with Six Sigma in these three years was examined. In order to be able to decide on changes in health and safety conditions, the company's analysis of air-flows and heat transfer using the exhaust gas temperature in a project aimed at optimizing workplace temperature control is exemplified.

Process hazard and decomposition mechanism of benzoyl peroxide in the presence of incompatible substances

Benzoyl peroxide (BPO) is a hazardous chemical, which is incompatible with many substances. Due to its high thermal sensitivity, it reacts violently with organic chemicals and can cause serious explosions when heated. Toluene and sodium hydroxide (NaOH) are incompatible substances frequently encountered in the production and utilization of BPO. Therefore, it is necessary to investigate the thermal decomposition behavior of BPO and its reaction mechanism under the effect of impurities. In this paper, thermal decomposition behavior of BPO, the mixture of BPO and toluene, and the mixture of BPO and NaOH were studied using differential scanning calorimetry and adiabatic calorimetry under different conditions. The decomposition kinetic models were obtained to simulate thermal runaway behavior in actual package. Chromatography-Mass Spectrometry experiments were performed to examine the composition of thermal decomposition products in different systems. The results show that NaOH improves the thermal stability of BPO and toluene makes it easier for BPO decomposition. Both NaOH and toluene change the decomposition path of BPO. The consumption of benzoic acid radicals is accelerated by NaOH. Toluene makes the decomposition of BPO follow an n-order reaction by consuming phenyl radicals generated by the decomposition of BPO. This study is important for understanding thermal hazards of BPO at the reaction mechanism level and ensuring process safety in industrial processes.

Exploration of the Relationships between the Spraying Condition and Wetting Behavior on Coal Surface of Dust Suppression Droplet: Improving the Utilization Rate

Dust suppression through water-based media is an important technical means, which is of great significance to industrial process safety and environmental protection. In order to improve the utilization rate of droplets, the dynamic spreading process of droplets impacting the coal surface was studied by the coupled level-set and volume-of-fluid methods (CLSVOF) method. The spread area was calculated by the binary method to characterize the wetting effect. Dimensionless spread area per unit volume (DSAPUV) was proposed to represent the utilization of droplets. The results show that the droplet spreading fracture process can be divided into three stages: initial deformation period, spreading fracture period, and stable period. When the particle size was not being changed, the area of dimensionless spread does not increase consistently with velocities, but there exists an optimal critical velocity of impingement, which is 17 m/s for the maximum dimensionless spread area reached by droplets with a diameter of 30 μm and 19 m/s for the maximum dimensionless spread area reached by droplets with a diameter of 50 μm. Droplet size is directly proportional to the dimensionless spread area. The maximum dimensionless spread areas of the droplets were all reached during spreading, and the time required increased gradually with increasing particle sizes. It was found that the effect of droplet size on the utilization of droplets was obvious when their size ranged from 10 μm to 50 μm, and their velocity ranged from 15 m/s to 20 m/s.

Experimental study on methane explosion suppression by heptafluoropropane drived modified ABC powder

The accidental methane explosion accident will not only pose a serious threat to the safety of industrial processes, but also cause serious environmental pollution with excessive carbon emissions and greenhouse effect. Therefore, this paper has studied the inhibition effect of modified ABC powder driven by heptafluoropropane on methane explosion. Multiple methane explosion suppression experiments were carried out using a 3.2 L horizontal experimental pipeline under various working condition. The experimental results demonstrate that the modified ABC powder has a certain inhibitory effect on methane explosions, but the explosive suppression effect of the powder improves when heptafluoropropane is used as the driving gas. When 3∼4 % heptafluoropropane is used as the driving gas, the flame is obviously inhibited. The deformation degree of flame shape is the largest at 4 % concentration. The flame totally folds inward at this point, forming an uneven finger flame. When 4 % concentration of heptafluoropropane drives the powder, there is a critical value of the concentration of the powder, which is 0.06 g/L. According to the characterization results, the average particle size of the modified ABC powder is less than 5 µm. It has good dispersibility and pyrolysis effect and is not easy to agglomerate. Under the driving action of heptafluoropropane, the powder will form aerosol, which can fully contact and react with the flame. The inert gas around the powder will reduce the forward speed of the flame and ensure that the flame and powder can fully contact and react. The combination of the two has a good synergistic effect and can effectively inhibit methane explosion.

Differences in perception of the importance of process safety indicators between experts in Iran and the West

Introduction: The importance of safety in high-risk industries such as oil and gas facilities has been reported previously. Process safety performance indicators can provide insight into improving the safety of process industries. This paper aims to rank the process safety indicators (metrics) by Fuzzy Best-Worst Method (FBWM) using the data gathered through a survey. Method: The study uses a structured approach considering the UK Health and Safety Executive (HSE), the Center for Chemical Process Safety (CCPS), and the IOGP (International Association of Oil and Gas Producers) recommendations and guidelines to generate an aggregate set of indicators. It calculates the level of importance of each indicator based on the opinions of experts from Iran and some Western countries. Results: The findings of the study demonstrate that some lagging indicators such as the number of times processes do not proceed as planned due to insufficient staff competence and the number of unexpected disruptions of the process due to failure in instrumentation and alarms are important in process industries in both Iran and Western countries. Western experts identified process safety incident severity rate as an important lagging indicator, whereas Iranian experts considered this as relatively unimportant. In addition, leading indicators such as sufficient process safety training and competency, the desired function of instrumentation and alarms, and proper management of fatigue risk play an important role in enhancing the safety performance of process industries. Experts in Iran viewed permit to work as an important leading indicator, while experts in the West focused on fatigue risk management. Practical Applications: The methodology used in the current study gives a good view to managers and safety professionals in regard to the most important indicators of process safety and allows them to focus more on important process safety indicators.

DIY liquid handling robots for integrated STEM education and life science research.

Automation has played a key role in improving the safety, accuracy, and efficiency of manufacturing and industrial processes and has the potential to greatly increase throughput in the life sciences. However, the lack of accessible entry-point automation hardware in life science research and STEM education hinders its widespread adoption and development for life science applications. Here we investigate the design of a low-cost (~$150) open-source DIY Arduino-controlled liquid handling robot (LHR) featuring plastic laser-cut parts. The robot moves in three axes with 0.5 mm accuracy and reliably dispenses liquid down to 20 μL. The open source, modular design allows for flexibility and easy modification. A block-based programming interface (Snap4Arduino) further extends the accessibility of this robot, encouraging adaptation and use by educators, hobbyists and beginner programmers. This robot was co-designed with teachers, and we detail the teachers' feedback in the context of a qualitative study. We conclude that affordable and accessible LHRs similar to this one could provide a useful educational tool to be deployed in classrooms, and LHR-based curricula may encourage interest in STEM and effectively introduce automation technology to life science enthusiasts.

An Intelligent Approach for Adaptive Liquid Level Control System Using Machine Learning

Abstract: Liquid level control is a mechanism that monitors, compares, and regulates the level of liquids within a process to a set value. Level measurement determines the position of the level relative to the top or bottom of the process fluid storage tank or silo. Level control and measurement are essential to assuring the safety and profitability of industrial processes. In this project, we are Implementing an intelligent mechanism using machine learning to predict the level of liquid in a reservoir based on the time and motor speed thus avoiding errors and keeping the process is ongoing. Keywords: Adaptive Liquid level control, Machine learning, Intelligent system, red

A novel imbalanced fault diagnosis method integrated KLFDA with improved cost‐sensitive learning ANBSVM

AbstractFault diagnosis, as an important approach to ensure the safety and stability of industrial processes, has been widely studied in recent years. During the running process, it is noted that the normal data are always much more than the fault data, which demonstrates imbalanced characteristics and leads to a negative effect on the overall accuracy of fault diagnosis. Targeting the problem, a novel imbalanced fault diagnosis method integrated kernel local Fisher discriminant analysis (KLFDA) with improved adaptive near‐Bayesian support vector machine (ANBSVM) is proposed in this paper. First, KLFDA is used to extract the non‐linear features while maintaining the local spatial structure of the data by introducing flow pattern learning. Second, considering the imbalance characteristics of the data, the data set is divided into a majority class (normal data) and a minority class (fault data). The density distributions of the two classes in their overlapping region are characterized by the proportional function of variance. Third, by minimizing the Bayesian error under the proportion function, the weight factors are adaptively obtained and then introduced into the objective function of the support vector machine (SVM). Namely, a cost sensitivity‐based ANBSVM classifier for fault diagnosis is constructed. Finally, by the simulation experiment on the Tennessee Eastman (TE) process, the comparison results show that the proposed ANBSVM‐based fault diagnosis method makes progress in the performance of fault diagnosis with higher diagnostic accuracy and F1 score.

An improved risk and reliability framework-based maintenance planning for food processing systems

ABSTRACT In food processing systems, it is necessary to evaluate the risk process and reliability in order to prevent unexpected events and control functional safety through well-planned maintenance. This study examines risk and reliability models and develops their theories using a novel framework based on failure behavior trends. The proposed framework was implemented in edible oil industries, specifically purification processes, and consists of three steps: first, identifying potential failures using risk-based approaches such as Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA); second, adapting a statistical structure consisting of homogenization survey and validity of identity and independence; and finally, estimating the failure rate and reliability to find the suitable maintenance intervals. The findings revealed that the proposed framework is capable of identifying critical equipment with high failure rates as the most important bottlenecks in edible oil processing lines, allowing maintenance plans to be carried out based on the various levels of reliability. Furthermore, opportunistic maintenance intervals were suggested due to the series configuration of equipment in such a process. As a result, the findings of this study may be useful in improving process safety and availability in the food processing industry.

Hazard identification, risk assessment and management of industrial system: Process safety in mining industry

Hazard identification, risk assessment and management are of great significance for the safe and efficient production of industrial system. However, due to the lack of quantitative methods, the production of industrial systems still lacks safety guarantees. Firstly, we revise the accident causation model of mining industry based on the “Swiss-Cheese” model and construct a three-dimensional multi-granular structure model applied to the mining industry. To identify hazards of mining industry, a multi-layer hierarchical structure model and a system safety assessment model are established according to finite covering theorem. Then, the hazard identification is carried out based on the multi-layer hierarchical model and the risk matrix method, and a quantitative method is proposed to calculate the process indicators. Subsequently, the weighted grey relational analysis method is used for risk assessment on the driving system in mining industry. To improve the safety level of the driving system, the Hazard Identification-Assessment-Check-Action risk closed-loop management mode is used for risk management in the purpose of obtaining hazard rectification measures. Risk assessment is carried out again after risk rectification, and the safety level of the driving system can reach higher level. This research can provide guidance for the process safety improvement of industrial systems.

Transfer Learning for Disruptive 5G-Enabled Industrial Internet of Things

Internet of things (IoT) and 5G network are fundamental building blocks for industrial IoT (IIoT). IoT has enabled real-time monitoring and actuation in industrial floors and machinery, aimed at improving the efficiency and safety of industrial activities and processes. On the other hand, 5G networks will provide ultra-reliable and low-latency communication for the wireless integration of autonomous industrial machinery, mobile vehicles, and robots, and management systems, aimed at the real-time control and management of industrial machinery toward smart factories. In IIoT, machine learning (ML) will also play a fundamental role in handling complex tasks at industrial machinery and 5G networks management, configuration, and control. However, ML suffers from the cold-start problem and needs a large amount of highly accurate data samples for model training, which is costly and difficult to obtain in IIoT applications. In this article, we shed light on the design of transfer learning (TL)-based systems for IIoT. We discuss how TL can overcome the demand for high-quality large data samples required to training ML models in IIoT. We also highlight the work principles and daunting challenges faced during the TL systems for IIoT. Furthermore, we categorize the TL systems for IIoT into TL for IIoT machinery level and for IIoT networking level and provide an in-depth discussion of the design building blocks and challenges of TL systems in each proposed class. Finally, we point out some future research directions for the design of novel TL-based systems for envisioned 5G-enabled IIoT applications.

Consequence assessment considerations for toxic natural gas dispersion modeling

The numerical simulation of gas dispersion and estimation of consequence impact is of importance in Oil and Gas industry's process safety management. For natural gas fields with toxic components like Hydrogen sulfide, the toxicity impact zone drives business decisions related to equipment design, facility siting, layout, land use planning, and emergency response measures. Proprietary tools or empirical models which are calibrated using experiment database are often used for carrying out consequence modeling.The selection of a software tool and a suitable dispersion model, based on the cloud behavior, at the source of dispersion is critical for the impact zone estimation. It is observed that, the fluid phase and the cloud density are key for determining the appropriate dispersion model. Incorrect parameter selection could lead to an inaccurate consequence impact zone estimation. This in turn could result in disproportionate process risk management efforts especially for toxic impacts from exposure to a very low concentration.This paper discusses the results from consequence modeling studies done for a selected set of toxic natural gas release events related to onshore pipeline transfer using approved software. The study analyses the modeling inputs, parameters and determines the key release source terms and atmospheric parameters that impacts the estimation of impact zones. The study determined that the natural gas dispersion behaviour is dependent on the natural gas molar mass and the composition of Hydrogen sulfide. The study provides the guidance on overcoming uncertainty in dispersion modeling through sensitivity assessments and lists key parameters to be subjected for toxic natural gas dispersion modeling sensitivity analysis.

Probabilistic Stationary Subspace Analysis for Monitoring Nonstationary Industrial Processes With Uncertainty

Actual industrial processes often show nonstationary characteristics, so nonstationary process monitoring is significant to ensure the safety and reliability of industrial processes. However, existing monitoring methods for nonstationary processes usually ignore process uncertainties, caused by random noises and unknown disturbances. It is worth noting that process uncertainties may degrade the monitoring performance for incipient faults, and result in over-fitting of model parameters. To address the problem of monitoring nonstationary industrial processes with uncertainty, a novel algorithm called probabilistic stationary subspace analysis (PSSA) is proposed in this article. PSSA explicitly models process uncertainties, and distinguishes actual process variations from the uncertainty. In view of the coupling between model parameters, the expectation maximization algorithm is used to estimate the parameters of PSSA, and the closed-form updates are derived in detail. Based on PSSA, two detection statistics are designed for process monitoring. Finally, the effective performance of the proposed method is demonstrated by three case studies, including a numerical example, a closed-loop continuous stirred tank reactor, and a real power plant at Zhejiang Provincial Energy Group of China.

Process safety education: Validating the concepts for a process safety program (article 2/2)

AbstractThis article focusses on the validation of the concepts that deserve integration in, for example, an Advanced Master of Safety Engineering program. The selection of the concepts was described in the previous article “Process safety education: Selecting the concepts for a process safety program.” This historical and theoretical selection process of process safety concepts needs a validation by the industry and review by root cause analysis of recent major incidents and accidents. The finally selected, reviewed, and adjusted list of concepts are the backbone to build on and improve short and 1‐year academic process safety courses and programs as well as industrial process safety trainings. Apart from the validation of the selected concepts, the industry validation also resulted in an extension and clarification of the concepts by experts in the subject matter and the desired competencies and characteristics of a young engineer educated in the process safety concepts.