Nex-Hys: minimum ignition temperature of hybrid mixtures

doi:10.1016/j.jlp.2021.104502

Journal of Loss Prevention in the Process Industries

Volume 72, September 2021, 104502

https://doi.org/10.1016/j.jlp.2021.104502 Get rights and content

Highlights

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Systematic research on the MIT in the GG furnace, analyzing reproducibility and reliability.
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Introduction of a new method to decide between ignition and non-ignition.
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Modification of the standard procedure to be able to test dust, gases and vapors in the GG furnace.
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First results for the MIT of dust and vapour hybrid mixtures.

Abstract

Although the minimum ignition temperature is an important safety characteristic and of practical relevance in industrial processes, actually only standard operation procedures are available for pure substances and single-phase values. Nevertheless, combinations of substances or mixtures are used in industrial processes and up to now it is not possible to provide a standardised minimum ignition temperature and in consequence to design a process safely with regard to the substances used.

In order to get minimum ignition temperatures for frequently used hybrid mixtures, first, the minimum ignition temperatures and ignition frequencies were determined in the modified Godbert-Greenwald furnace for two single phase solids and a liquid substance. Second, minimum ignition temperatures and ignition frequencies were determined for several combinations as hybrid mixture of dust and liquid.

In parallel to the determination of ignition temperatures a new camera and computer system to differentiate ignition from non-ignition is developed. First results are promising that such a system could be much less operator depended.

By a high number of repetitions to classify regions of ignition the base is laid to decide about a new procedure for a hybrid standard and updating existing ones, too. This is one of the necessary aims to be reached in the Nex-Hys project.

A noticeable decrease of minimum ignition temperatures below the MIT of the pure solids was observed for the one hybrid mixture tested, yet. Furthermore more widely dispersed area of ignition is shown. In accordance to previously findings, the results demonstrate a strong relationship between likelihood of explosion and amount of added solvent. In consequence the hybrid mixture is characterized by a lower minimum ignition temperature than the single dust.

Introduction

The impact of mixtures of burnable dusts and liquids (hybrid mixtures) on safety characteristics is important for several industrial processes. Safety characteristics and operational procedures are based on knowledge about the ignition potential and explosion characteristics (Addai et al. (2015b)). Although efforts have been made in recent years by various research groups to generate parameters for hybrid mixtures, most values are often based on the properties of pure substances.

As already emphasized by the fundamental work of Bartknecht (1981), a two-component mixture of a flammable dust and gas can be ignited below the lower explosion limit of a single component of the mixture. The minimum ignition temperature (MIT) of dust mixtures was experimentally addressed by Zunaid (2013). A substantial amount of spark formation below the MIT was reported which might be an ignition resource for hybrid mixtures. Dufaud et al. (2008, 2009) reported effects of ignitable dust-vapour-mixtures to explosions with a greater severity than the ones of both compounds taken separately.

A substantial increase of knowledge about the behaviour of hybrid mixtures has been made by the research of Addai et al. who has published various experimental results that focused on the lower explosion limits (Addai et al. (2015a, b, 2017a, b); Abbas et al. (2019)), the minimum ignition temperature (Addai et al. (2017a, 2016c, d)), the minimum ignition energy (Addai et al. (2016b, d)), and the minimum explosion concentration (Addai et al. (2015a; 2017b)) of hybrid mixtures respectively.

Gabel and Krause (2019) recently presented findings of minimum ignition temperature for premixed dust-solvent mixtures. They came up with an impact of the phase conditions of the components of the mixture on the MIT.

The presence of hot surfaces in combination with ignitable dust clouds is frequently relevant in industrial processes (e.g. sawing, cutting, grinding, drying or heating of particles). Therefore, the MIT is highly technical relevant as a benchmark for the probability of an ignition (citeAmyotte.2013) and appropriate to characterise the safety of a process against explosion risks. The state of research may be summarised as the ignition capability of a hybrid substance mixture is rather influenced by the ignitability of the gas phase than by a linear combination of the individual substances. Therefore it is questionable.

1.
That the variations and replications specified in the standard for determining the MIT of pure dust can be applied to hybrid mixtures without any adjustments;
2.
That the visually determined Boolean operator used in the standard procedure – ignition or non-ignition by a visible flame that exits from the furnace tube – is applicable to hybrid mixtures; and
3.
That the MIT of hybrid mixtures is characterised by a distinct range of reliable ignition/non-ignition frequencies.

In order to answer the raised questions, the aim of this contribution is to investigate the MIT of the pure substances (Lycopodium, dehumidified maize starch and n-Heptane) and a hybrid combination of maize starch and n-Heptane in a modified Godbert-Greenwald-furnace (GG furnace) with frequent repetitions.

Section snippets

Sample characteristics

Studies with frequent repetitions of single measurements were performed for dehumidified maize starch, Lycopodium and n-Heptane. The particle size distribution for two samples of starch and Lycopodium was determined by the principles of dynamic image analysis (Camsizer XT, Retsch Technology) according to ISO 13322–2:2006–11 (International Standards Organisation (2006)), cf. Fig. 1. A median particle size of approximately 0.03 mm for Lycopodium was determined. The median particle size of

Results and discussion

Each experimental trial, defined by the amount of substance $m / g$ or $V / μ L$ , the temperature of the furnace $T / \circ C$ , and the injection over-pressure $p^{+} / b a r$ , was repeated ten times. This high amount of repetitions was necessary in order to analyse frequencies of ignitions and to define ranges of transitions. Later investigation will be carried out with a reduced number of trials based on the new procedure proposed according to the primary findings.

Starting with pure Lycopodium the following

Conclusions and outlook

The experimental setup presented here is provided to determine the minimum ignition temperature of hybrid mixtures. It aims to create a standardised experimental setup and operation procedure. Starting with the analysis of the MIT of well-defined single-substance systems to generate a comparable and stable basis for parameter variations, in a second step a hybrid mixture consisting of Lycopodium and n-Heptane was tested. As expected, the MIT reduces significantly up to the order of magnitude of

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work has been performed within the research program ‘Development of standardised determination methods for safety parameters of explosion protection for hybrid mixtures (NEX-HYS)’ funded by the German Federal Ministry of Economic Affairs and Energy - BMBi (grant number 03TNH006A). The authors would like to thank SaiLokesh Pinjala, Ajinkya Kunjir and Sinha Shruti for their great effort in carrying out the laboratory experiments.

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A numerical model for the prediction of the minimum ignition temperature of dust clouds
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Therefore, a better understanding of the hot surface ignition and the MIT is of great importance for the prevention of dust explosion incidents. Numerous experimental studies on the MIT of dust clouds have been undertaken in recent years (Nifuku et al., 2007; Cao et al., 2012, 2019; Boilard et al., 2013; Mittal, 2014; Janès et al., 2014; Miao et al., 2016; Mishra and Azam, 2018; Deng et al., 2019; Tan et al., 2020; Wang et al., 2020; Sun et al., 2020; Gabel et al., 2021 and Krietsch et al., 2021). Particle size and dust concentration are the most intensively studied influencing parameters (Nifuku et al., 2007; Mittal, 2014; Mishra and Azam, 2018; Cao et al., 2019).
This paper presents a numerical model for the prediction of the minimum ignition temperature (MIT) of dust clouds. First, a physical model is developed for the dust cloud ignition in the Godbert-Greenwald furnace. A numerical approach is then applied for the MIT prediction based on the physical model. The model considers heat transfer between the air and dust particles, the dust particle reaction kinetics, and the residence times of dust clouds in the furnace. In general, for the 13 dusts studied, the calculated MIT data are in agreement with the experimental values. There is also great accordance between the experimental and numerical MIT variation trends against particle size. Two different ignition modes are discovered. The first one consists in ignition near the furnace wall for bigger particles characterized by rather short residence times. In the second mode, the ignition starts from the center of the furnace by self-heating of the dust cloud for smaller particles with longer residence times. For magnesium, as dust concentration increases, the lowest ignition temperature of the dust cloud IT(conc) decreases first, then transits to increase at a certain point. The transition happens at different dust concentrations for different particle sizes. Moreover, the MIT of the magnesium dust cloud generally increases as particle size increases, but the increasing trend stagnates within a certain medium particle size range.
Comparison between a numerical model and the classic thermal explosion theories for the calculation of the minimum ignition temperature of dust clouds
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With such an important role for the dust explosion risk evaluation, the dust cloud MIT has been studied intensely in recent years. Experimental studies of the dust cloud MIT are becoming rather mature with the development of the dust cloud MIT testing equipment (Nifuku et al., 2007; Cao et al., 2012; Boilard et al., 2013; Mittal, 2014; Janès et al., 2014; Miao et al., 2016; Addai et al., 2016a, 2016b, 2017; Mishra and Azam, 2018; Cao et al., 2019; Deng et al., 2019; Tan et al., 2020; Wang et al., 2020; Sun et al., 2020; Gabel et al., 2021; Krietsch et al., 2021; Wang et al., 2021; Wu et al., 2021; Arshad et al., 2022, etc.). The Godbert-Greenwald furnace (G-G furnace) is the most commonly used MIT testing apparatus (Eckhoff, 2019b).
A numerical model for the calculation of the minimum ignition temperature (MIT) of dust clouds is compared with the classic Semenov and Frank-Kamenetskii thermal explosion theories. The numerical model is developed based on the MIT testing equipment: Godbert-Greenwald furnace. The Semenov theory for uniform temperature system is exploited for the MIT calculation of a single dust particle (MITP). The Frank-Kamenetskii theory for a uniform system with temperature gradient is modified for the MIT calculation of a dust cloud (MITC). At stoichiometric dust concentration with infinite dust cloud residence time, two horizontal asymptotes are discovered along with the variation trend of the numerical ignition temperature against ignition delay time. The higher asymptotic temperature is only discovered for metal dusts. This temperature is almost identical to the MITP value with the Semenov theory. On the other hand, the lower asymptotic temperature for both metal and organic dusts almost equals the calculated MITC value with the Frank-Kamenetskii theory. Thus, there is good agreement between the numerical model and the classic thermal explosion theories. This study can provide reference for the theoretical prediction of the MIT of dust clouds.
SVM, ANN, and PSF modelling approaches for prediction of iron dust minimum ignition temperature (MIT) based on the synergistic effect of dispersion pressure and concentration
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Another recent experimental investigation has been conducted for the assessment of MIT for a hybrid mixture of dust and liquid. It was found that the MIT for a hybrid mixture tends to decrease considerably increasing the ignition risk than the single dust (Gabel et al., 2021). However, in the current research, the machine learning approach has been adopted to predict the MIT since the explosion is a statistical phenomenon and the experimental process is time-consuming and costly.
Data-driven models for predicting fire and explosion-related properties have been improved greatly in recent years using machine-learning algorithms. However, choosing the best machine learning approach is still a challenging task. Therefore, in this study, the predictability comparisons have been made with the different machine learning methods used to model the MIT for iron dust. The MIT of iron dust was determined using the Godbert-Greenwald furnace for seventy unique combinations of dispersion pressures and dust concentrations. The data has been divided into 'Training Set' and 'Testing Set'. The implementation and efficacy of machine learning and statistical approaches have been demonstrated through real-time experimental results. The support vector machines (SVM) regression models were trained with various kernel functions to enhance the performance of the resultant model. The cubic kernel function was found suitable for training SVMs. Besides, a feed-forward artificial neural network with the backpropagation algorithm and a polynomial surface fit model have also been developed to predict the MIT. For statistical phenomena, such as MIT, predictive modelling based on real-time experimental data is critical. If an accurate estimate of the combustible dust's minimum ignition temperature is confirmed, it is possible to ensure that the temperatures of the surrounding hot surfaces do not rise to that level, preventing an explosion. An overall comparison of predictive models has been given with unseen test data set. All the trained models yielded comparable results with unseen test data set. However, the SVM model with Bayesian optimizer approach can effectively assess the risk of ignition based on dust MIT under the influence of dispersion pressure and dust cloud concentration among all the approaches adopted in this study.
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Nex-Hys: minimum ignition temperature of hybrid mixtures

Highlights

Abstract

Introduction

Section snippets

Sample characteristics

Results and discussion

Conclusions and outlook

Declaration of competing interest

Acknowledgements

J. Loss Prev. Process. Ind.

Process Saf. Environ. Protect.

Process Saf. Environ. Protect.

Process Saf. Environ. Protect.

J. Loss Prev. Process. Ind.

J. Hazard Mater.

J. Hazard Mater.

J. Loss Prev. Process. Ind.

Powder Technol.