Gas Detector Placement Research Articles

Efficient Gas Leak Simulation Surrogate Modeling and Super Resolution for Gas Detector Placement Optimization

This study introduces an innovative framework that utilizes a limited number of sensors to detect chemical leaks early, enabling a timely and appropriate initial response. Consequently, it mitigates the risk of major industrial disasters. This framework integrates a surrogate model based on differentiable physics with a sensor placement optimization tool. Particularly noteworthy is that the surrogate model provides faster results compared to traditional CFD tools and seamlessly integrates with a CNN-based deep learning model, yielding higher-resolution outputs. This enhances the feasibility of practical application compared to conventional methods. Sensor placement optimization employs MINLP, considering various constraints to achieve more precise optimization. The proposed framework offers an innovative approach to address industrial safety concerns by enabling early chemical leak detection and facilitating timely responses, ultimately reducing risks like fires, explosions, and toxic incidents.

Developing a multi-disciplinary approach to risk-based mapping (RBM) in an onshore refinery: A case study on optimal gas detector placement

Ensuring fire and explosion safety in petrochemical facilities is a significant challenge due to the complexity of the environment. Input is required from operators, engineers, software developers and standards to guide the work. Risk-based mapping (RBM) provides a holistic approach to developing solutions. In this paper RBM is applied to the problem of gas detector placement in an onshore refinery based on a facility-wide analysis. This is done as an alternative to prescriptive geographic-based detector placement. The inputs from different disciplines are identified and a framework for a multi-disciplinary approach is proposed. The case study considers twenty-six processing units and two tank farms, comprising 1400 pieces of equipment, which were assessed and gas detectors were placed to reduce the risk of undetected gas by 80–90%. Computational Fluid Dynamics (CFD) analyses were incorporated to explicitly consider factors such as leak source location, fluid properties, nature of the release (high pressure or spillage), topography, air movement (wind speed and direction), ambient temperature and obstructions (equipment, buildings and structures). Both the likelihood and consequence of events are considered. A key observation from the RBM study was that for atmospheric storage tank farms, the size of the risk volumes must be carefully considered to allow for the timeous detection of gas clouds. Transient analysis of gas cloud growth up to predefined limits was used for this purpose. The work provides a framework to consider multiple representative loss of containment scenarios and yields quantitative evidence to optimize not only the placement of detectors, but also the layouts and mitigation and emergency response measures.

Optimization of gas detectors placement in complex industrial layouts based on CFD simulations

The optimal placement of hazardous gas detectors plays a relevant role in the prevention and mitigation of critical scenarios among chemical and process facilities. However, the operative performance of gas sensors networks is affected by the complexity of process facilities layout and sensors’ reliability. Moreover, economic constraints may bias the selection of the optimal number of sensors. The present work deals with the development of a methodology to support the selection and positioning of hazardous gas detectors in complex industrial layouts. The methodology exploited preliminary computational fluid dynamics (CFD) simulations to quantify the extension of hazardous gas clouds. A comparison among gas dispersion studies based on CFD and integral models was also carried out to demonstrate the limitation of adopting simplified analyses on the evaluation of gas dispersion hazards. The optimization methodology was then developed based on a twofold algorithm. The first algorithm aimed at determining the optimal sensors positions, seeking for the locations that maximize the detection frequency for a given number of sensors. The second algorithm adopted a multicriteria decision analysis based on the reference point method to find the optimal economic number of sensors and, thus, the most cost-effective strategy. A case study located in a real gasoline storage plant was analysed. The results demonstrated the advantage of adopting a sound quantitative evaluation of the number of detectors integrating economic and risk-based evaluations.

A fuzzy multi-objective optimisation model of risk-based gas detector placement methodology for explosion protection in oil and gas facilities

A flammable gas detection system is one of the critical control strategies of catastrophic events such as fire and explosion. While gas detector technology has improved significantly, adopting a methodology for optimal placement of gas detectors is still an issue, especially when integrated with a risk-based approach. An enhancement of a risk-based approach is proposed to optimise the placement of flammable gas detectors by integrating a formulation of fuzzy multi-objective mixed-integer linear programming with the goal of minimising the residual risk and total number of detectors for effective explosion protection. The proposed methodology primarily begins with the identification of critical leak scenarios that require detection followed by the prediction of a targeted gas cloud and dispersion analysis using a computational fluid dynamic model. Risk analysis is conducted to identify high risk areas that need flammable gas detectors protection, which is the input for the mathematical model. The proposed risk-based model was tested using a case study involving a natural gas liquids (NGL) recovery unit, and the results were compared to a published greedy algorithm (GA) formulation. By using mixed-integer linear programming (MILP) formulation, the number of detectors needed are lower with higher risk reductions compared to the GA formulation. Additionally, a sensitivity analysis was performed to determine the proposed model’s response to parameter variations.

An experimental approach for verifying the effectiveness of the optimized scheme of gas detector placement in process installations

The research on optimization for gas detectors placement has addressed great attention in recent years. The steps of optimization include setting up a leak scenario library, predicting the leakage and dispersion consequences by Computational Fluid Dynamics (CFD) simulation, and employing the stochastic programming (SP) method to find the optimal layout scheme. In this paper, an experiment approach of verifying the effectiveness of the optimized gas detector placement is proposed. Firstly, a scale-down process experiment unit is built and a similar CFD model has been set up to predict the gas leakage consequences. Secondly, the optimal scheme is acquired by the CFD simulation results and the SP model. Thirdly, the position to install gas sensors in the experiment unit is determined by the locations of the monitor points in the CFD simulation. Gas leakage and dispersion experiments are conducted, and the sensor readings of gas concentration are recorded. Finally, the comparison results of the detection time of all schemes demonstrate that the optimized scheme obtained by the SP approach has a better performance than most other nonoptimal schemes. This research is significant in providing support for the validity of the current SP model in gas detector placement optimization problems.

A comparative study of optimization models for the gas detector placement in process facilities

Gas detector network is an important layer of protection in process facilities for prevention gas leakage accidents. But traditional standards just provide basic principles for the installation of detectors. In this study, three stochastic programming (SP) models are developed and contrasted, namely minimal detection time P-Median model (MDTP), minimal leakage concentration P-Median model (MLCP), and minimal individual risk P-median model (MIRP). Meanwhile all possible leak scenarios are identified based on the combination of wind field set and leakage sources. And clustering analysis is used to filter similar scenarios and select representative leak scenarios. The leak consequences are predicted by computational fluid dynamics (CFD) method and the results are served as the input data of these SP models. A case study is carried out in a diesel hydrogenation unit.

Optimization of gas detector placement considering scenario probability and detector reliability in oil refinery installation

Gas detection system is a critical layer of protection in process safety. Leak scenario probability and detector reliability are two key factors in the optimization of gas detector placement. However, they are easily neglected in previous studies, which may lead to an inaccurate evaluation of the optimization solutions. In this study, a stochastic programming (SP) optimization method is proposed considering these two factors. In order to quantitatively represent the probability of leak scenarios, a complete accident scenario set (CASS) is built combining leak sources and wind fields. Then, the computational fluid dynamics (CFD) method is adopted for consequence modeling of gas dispersion. The Markov model is developed to predict the detector reliability. With the objective of minimal cumulative detection time (MCDT), the SP formulation considering scenario probability and detector reliability (MCDT-SPR) is proposed. By introducing the particle swarm optimization (PSO) algorithm, the optimization formulations can be solved. A case study is investigated on a diesel hydrogenation refining unit. Results validate this approach is promising to improve the detection efficiency. This method is more practical and matching with the actual industrial environment, where the leak scenarios and the detector reliability can change dynamically in real process setting.

Monitor Points Selection in CFD FLACS for Gas Detector Placement

Gas detector first invented was in 1815 to detect the presence of the methane gas and becomes part of a safety system when it is capable to detect the gas leakage and decrease the risk of major accident occurrence. However, the efficiency of the gas detector has been questioned among industry people due to unable to measure the effectiveness of the gas detector quantitatively. Industry people has a problem on how many and where should they locate the gas detector. This study explained the very beginning steps on how to determine the number and location of the gas detector should be installed. This research simulated the gas explosion cloud by using CFD FLACS at highly hazardous area by setting the four parameters with different values of wind speed, wind direction, leak rate and leak direction. In order to optimize the placement of the gas detector, three objectives need to be achieved: 1) to obtain the fastest response time of the gas detector to any gas leakage, 2) to ensure the availability of the gas detection system in worst conditions and 3) to place the gas detector in the potentially hazardous area. The locations of the gas detector meet the objectives based on the approach applied in this study.

Multi-objective optimization considering cost-benefit ratio for the placement of gas detectors in oil refinery installations

It is of great importance for an oil refinery installation to have an effectively designed gas detection system. The majority of current research on the optimization of gas detector placement focuses on a single-objective stochastic programming (SP) approach. However, it is actually a multi-objective optimization problem, trying to obtain optimal solutions with conflicting criteria. This paper proposes a multi-objective optimization method for detector placement, achieving a balance between the detection efficiency improvement and the required investment. Based on the leakage scenario and the consequence modelling, the cumulative death probability (CDP) in the whole unit is quantitatively represented. The reduced fatality can also be evaluated according to the detection time. Then, a cost-benefit ratio (CBR) model of the gas detector investment and benefit from fatality reduction can be established. With the objective of minimizing both the CDP and CBR, a multi-objective layout optimization model considering the cost-benefit (MLOM-CB) is proposed. A non-dominated sorting genetic algorithm-II (NSGA-II) is used to obtain acceptable results. The optimization of hydrogen sulfide gas detector placement is taken as a case study. Results demonstrate that this approach can improve the detection efficiency of gas detector networks and the flexibility of decision-making for gas detector investment.

A stochastic programming approach for the optimization of gas detector placement in offshore platforms

In offshore platforms, effective gas detection system is an essential layer of protection for preventing potential gas release accident from turning to fire and explosion disasters. However, according to statistics the actual efficiency of gas detection systems is still unsatisfactory. In this work, a stochastic programming (SP) approach is proposed to arrange gas detectors in optimal places. Combined with meteorological data and the hydrocarbon releases (HCR) database, the equipment leakage scenarios are envisaged. The computational fluid dynamics (CFD) commercial code Ansys-Fluent is used to simulate selected scenarios and predict the consequences. With the objective of minimizing the cumulative risk of gas leakage scenarios, a stochastic mixed-integer linear programming formulation based on p-median value theorem is proposed. Then particle swarm optimization (PSO) algorithm is used to solve the optimization model. The gas detector placement optimization on an oil drilling production platform (DPP) is also carried out as a case example. This methodology can be used in the design phase of offshore platform and can hopefully enhance the performance of the gas detection system.

Optimum placement of gas detectors considering voting strategy with different detection set points

Voting logic or k out of p redundancy is widely used in safety systems to meet two key objectives: enhancing reliability and reducing spurious performance. Even so, in designing gas detector systems especially of flammable gas type, it is a common engineering practice to consider at least two different detection levels in addition to normal voting. Under such circumstances, the presence of gas is confirmed if sensed by two detectors at two different concentrations: one at a low level and another one in the same voting network at a high level. Existing formulations for optimal placement of gas detectors do not consider this type of voting. In this study, first the general form of multi-level voting arrangement (mlv) with arbitrary levels of detection is defined. Then, two extended integer linear programming (ILP) formulations are proposed to address mlv in optimal gas detector placement. The first one called MCLPm maximizes coverage, and the second one called SP-Vm seeks to minimize an expected damage parameter such as detection time against a set of pre-defined leak scenarios. In a simple test case, the proposed formulations are examined, verified and compared with the original (non-extended) formulations. The results confirm the necessity and importance of using the extended formulations when mlv has been applied as the desired type of voting.

A risk-based methodology for the optimal placement of hazardous gas detectors

Abstract Hazardous gas detection systems play an important role in preventing catastrophic gas-related accidents in process industries. Even though effective detection technology currently exists for hazardous gas releases and a majority of process installations have a large number of sensitive detectors in place, the actual operating performance of gas detection systems still does not meet the expected requirements. In this paper, a risk-based methodology is proposed to optimize the placement of hazardous gas detectors. The methodology includes three main steps, namely, the establishment of representative leak scenarios, computational fluid dynamics (CFD)-based gas dispersion modeling, and the establishment of an optimized solution. Based on the combination of gas leak probability and joint distribution probability of wind velocity and wind direction, a quantitative filtering approach is presented to select representative leak scenarios from all potential scenarios. The commercial code ANSYS-FLUENT is used to estimate the consequence of hazardous gas dispersions under various leak and environmental conditions. A stochastic mixed-integer linear programming formulation with the objective of minimizing the total leak risk across all representative leak scenarios is proposed, and the greedy dropping heuristic algorithm (GDHA) is used to solve the optimization model. Finally, a practical application of the methodology is performed to validate its effectiveness for the optimal design of a gas detector system in a high-sulfur natural gas purification plant in Chongqing, China. The results show that an appropriate number of gas detectors with optimal cost-effectiveness can be obtained, and the total leak risk across all potential scenarios can be substantially reduced. This methodology provides an effective approach to guide the optimal placement of point-type gas detection systems involved with either single or mixed gas releases.

A global stochastic programming approach for the optimal placement of gas detectors with nonuniform unavailabilities

Optimal design of a gas detection systems is challenging because of the numerous sources of uncertainty, including weather and environmental conditions, leak location and characteristics, and process conditions. Rigorous CFD simulations of dispersion scenarios combined with stochastic programming techniques have been successfully applied to the problem of optimal gas detector placement; however, rigorous treatment of sensor failure and nonuniform unavailability has received less attention. To improve reliability of the design, this paper proposes a problem formulation that explicitly considers nonuniform unavailabilities and all backup detection levels. The resulting sensor placement problem is a large-scale mixed-integer nonlinear programming (MINLP) problem that requires a tailored solution approach for efficient solution. We have developed a multitree method which depends on iteratively solving a sequence of upper-bounding master problems and lower-bounding subproblems. The tailored global solution strategy is tested on a real data problem and the encouraging numerical results indicate that our solution framework is promising in solving sensor placement problems. This paper was selected for the special issue in JLPPI from the 2016 International Symposium of the MKO Process Safety Center.

A risk-based methodology for optimum placement of flammable gas detectors within open process plants

Although the main objective of utilizing gas detection systems is risk reduction by detecting high-risk scenarios, the majority of published placement procedures do not address the risk concept quantitatively. To include this concept, a risk-based methodology is proposed that consists of four key steps: input data, dispersion analysis, risk analysis, and optimization. In the first step, a set of release scenarios are defined and required data, including frequency of release, wind rose, and grid set, are provided. In the dispersion analysis step, the set of scenarios is simulated using a dispersion simulation tool and the ability of grid points to detect any scenario is stored in a binary matrix called detection matrix (DI). In the risk analysis, the risk of each scenario is calculated incorporating these factors: frequency, damage to personnel, asset loss, and probability of delayed ignition. Once the DI and risk of scenarios are provided, at the last step, optimum placement is performed using a risk-based objective function, which is defined as the sum of the risk of undetected scenarios. The optimization formulation called maximum risk reduction is solved by a greedy approach known as dynamic programming in which the location of detectors is determined by an iterative procedure: each time finding the grid point that can cover maximum undetected risk. The applicability of the methodology is shown in a case study and the results are compared with a coverage-based formulation.

Optimal placement of gas detectors: A P‐median formulation considering dynamic nonuniform unavailabilities

A stochastic programming formulation (SPqt), based on the P‐median problem, is proposed for determining the optimal placement of detectors in mitigation systems while considering nonuniform dynamic detector unavailabilities. Unlike previously proposed formulations, SPqt explicitly considers backup detection levels. This allows the modeller to determine the maximum degree of the nonlinear products to be used based on the trade‐off between computational complexity and solution accuracy. We analyze this trade‐off on formulation SPqt results by using 4 real data sets for the gas detector placement problem while using unavailability values obtained from real industry gas detector data. For this data, our results show that two detection levels are sufficient to find objective values within 1% of the optimal solution. Using two detection levels reduces the nonlinear formulation to a quadratic formulation. Three solution strategies are proposed for this quadratic formulation and then compared from the computational efficiency perspective. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2728–2739, 2016

A quantitative assessment on the placement practices of gas detectors in the process industries

Gas detection is an important safety system with interfaces to several other safety safeguards. However, the generality of the regulations, standards and recommended practices in conjunction with the inherent challenges of the gas detector placement problem, has resulted in a widespread use of prescriptive and qualitative detector placement strategies. In order to take advantage of the quantitative information provided by dispersion simulations, a stochastic programming formulation (SP-UV) was previously proposed, developed and validated by the authors. This formulation identifies the gas detector layout that minimizes the expected value of an overall damage coefficient (i.e., the minimization of a risk metric) given a set of dispersion scenarios. Results demonstrated the potential and suitability of numerical optimization to solve the gas detector placement problem while rigorously considering its inherent uncertainties. In this work, four existing approaches for gas detector placement were implemented and compared with the previously proposed quantitative optimization-based approach using three different performance metrics in accordance to the objectives of gas detection systems. Results provide evidence on the effectiveness of the use of dispersion simulations, and mathematical programming, to supplement the gas detector placement problem.

A Stochastic Programming Approach for the Optimal Placement of Gas Detectors: Unavailability and Voting Strategies

The task of detector placement is especially difficult due to the large number of variables that influence the risk associated with gas leaks; these include leak conditions, fluid properties, dispersion characteristics, process equipment geometry, and detection equipment. Existing work on optimal gas detector placement does not take into account two key considerations associated with gas detector equipment and policies: the possibility that the detector is not able to perform its intended function and the requirement for a voting logic. Two stochastic Mixed-Integer Linear Programming (MILP) formulations, SP-U and SP-UV, are proposed to address this issue. Formulation results are presented and compared with optimal placement results from a formulation that ignores these two considerations. Unavailability and voting logic considerations result in changes to the optimal detector placement, and significant improvements in the expected time to detection when false positives and false negative alarms are acknowledged.

A methodology for determining efficient gas detector locations on offshore installations

More than 70% of accidents that occur on offshore installations are result of hydrocarbon explosions and fires, which, because they involve blast effects and heat, are extremely hazardous and have serious consequences in terms of human health, structural safety and the surrounding environment. Most such accidents are caused by gas leaks, which, if undetected, can lead to the formation of a concentrated gas cloud that can ignite or explode. An effective gas detection system is important for preventing gas-related catastrophic accidents and can mitigate risk on offshore installations. The aim of this study is to develop a risk-based methodology to aid the initial placement of gas detectors for an efficient gas detection system that will function optimally in all possible scenarios on offshore installations.

Optimal gas detector placement under uncertainty considering Conditional-Value-at-Risk

A stochastic programming formulation considering Conditional-Value-at-Risk (CVaR) is developed for the optimal placement of gas detectors in petrochemical process facilities. A rigorous gas dispersion simulator, FLACS, is used to generate release scenario data for a real process geometry. We consider two problem formulations: minimization of expected detection time and minimization of expected detection time subject to a restriction on CVaR across the scenario set. The extensive form of each stochastic program is formulated in Pyomo and solved using CPLEX. Considering all scenarios, we compare key values and histograms of detection times for both formulations. Minimizing the mean detection time only can lead to optimal detector placements with a good expected behavior, but unacceptable worst-case behavior. The formulations that minimize or constraint CVaR produce sensor placements with significantly better worst-case behavior and fewer scenarios having high detection times. Considering these results, a strong case for the use of optimal sensor placement using stochastic programming considering CVaR is made for improving safety systems.

A stochastic programming approach for gas detector placement using CFD-based dispersion simulations

A stochastic programming formulation is developed for determining the optimal placement of gas detectors in petrochemical facilities. FLACS, a rigorous gas dispersion package, is used to generate hundreds of scenarios with different leak locations and weather conditions. Three problem formulations are investigated: minimization of expected detection time, minimization of expected detection time including a coverage constraint, and a placement based on coverage alone. The extensive forms of these optimization problems are written in Pyomo and solved using CPLEX. A sampling procedure is used to find confidence intervals on the optimality gap and quantify the effectiveness of detector placements on alternate subsamples of scenarios. Results show that the additional coverage constraint significantly improves performance on alternate subsamples. Furthermore, both optimization-based approaches dramatically outperform the coverage-only approach, making a strong case for the use of rigorous dispersion simulation coupled with stochastic programming to improve the effectiveness of these safety systems.