Integrated HAZard & OPerability study (HAZOP) and Adaptive Neuro Fuzzy Inference System (ANFIS) as an early alarm framework for Glycerin emission control of a chemical plant during floods: A case study of Liberec city, Czech republic
Authors: Moezzi Reza , Taghavian Hadi , Gheibi Mohammad , Koci Jan , Cyrus Jindrich 
Published: February 2023

&lt;p&gt;The release of dangerous chemicals in flood crises is a common and recurring phenomenon. Because due to the breakdown of the infrastructure in the year, the possibility of release of dangerous pollutants is expected more than before [1]. The present research has chosen a chemical factory with Glycerin tanks as a case study in the city of Liberec (Czech Republic). As acute exposure to these compounds leads to effects such as headaches, dizziness, bloating, nausea, vomiting, thirst, and diarrhea [2], the control of this pollution can significantly reduce the health risk impacts especially during floods. In the first step of this study, all the physics coordinates of the reservoirs were examined and evaluated in different conditions. In the next step, according to the experimental calculations, the volume of Glycerin release from the reservoirs and in the flood flow was evaluated. Risk analysis was done using the HAZard &amp; OPerability study (HAZOP) technique. Risk Number in the HAZOP method is computed based on two factors containing Risk Possibility (RP) and Risk Intensity (RI) [3]. Both the values are determined according to prediction of Adaptive Neuro Fuzzy Inference System (ANFIS) [4] based on previous studies in different countries. The results demonstrated that the integrated HAZOP-ANFIS model has different performance in various flood flow conditions.&lt;/p&gt;
&lt;p&gt;RP value is predicted abased on three parameters include; rainfall value, mass of contaminant, and flood flow. Likewise, the RI is estimated in ANFIS method according to self-assimilative factor and continuity of floods [4]. The computations demonstrated that ANFIS technique has more than 0.9 correlation coefficient for prediction of both flood risk factors (RP and RI). Likewise, the sensitivity analysis of the prediction system is examined as per all the declared physical features which effects on RP and RI. Also, it should be mentioned that in the Glycerin content is in the range of 45%- 65% in the case study. Numerical analysis illustrated the performance of designed framework has more efficiency in the higher concentrations of the contamination. The suggested structure can be used as an early qualitative framework for acute effects of hazardous material emissions.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; &lt;em&gt;HAZOP risk assessment; ANFIS; Flood; Glycerin emissions; Chemical plant&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;References:&lt;/p&gt;
&lt;p&gt;&lt;em&gt;[1] Yard, E.E., Murphy, M.W., Schneeberger, C., Narayanan, J., Hoo, E., Freiman, A., Lewis, L.S. and Hill, V.R., 2014. Microbial and chemical contamination during and after flooding in the Ohio River&amp;#8212;Kentucky, 2011. Journal of Environmental Science and Health, Part A, 49(11), pp.1236-1243.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;&lt;em&gt;[2] Dunj&amp;#243;, J., Fthenakis, V., V&amp;#237;lchez, J.A. and Arnaldos, J., 2010. Hazard and operability (HAZOP) analysis. A literature review. Journal of hazardous materials, 173(1-3), pp.19-32.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;&lt;em&gt;[3] Zabihi, O., Siamaki, M., Gheibi, M., Akrami, M. and Hajiaghaei-Keshteli, M., 2023. A smart sustainable system for flood damage management with the application of artificial intelligence and multi-criteria decision-making computations. International Journal of Disaster Risk Reduction, 84, p.103470.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;&lt;em&gt;[4] Akbarian, H., Gheibi, M., Hajiaghaei-Keshteli, M. and Rahmani, M., 2022. A hybrid novel framework for flood disaster risk control in developing countries based on smart prediction systems and prioritized scenarios. Journal of environmental management, 312, p.114939.&lt;/em&gt;&lt;/p&gt;