PROCEEDINGSof the 5th International Conference on Chemical Technology

www.icct.cz

10. – 12. 4. 2017Mikulov, Czech Republic

5th International Conference on Chemical Technology

www.icct.cz

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As it can be seen in Figure 4, none of the proposed operating points is located in the safety operating regime for every uncertainty of the reaction enthalpy in the studied value range. However, if the reaction enthalpy was decreased only by 5 %, all proposed operating points ae still satisfactory. In case of a reaction enthalpy decrease by 10 %, operating point F was shifted to the hazardous operating regime because of the reactor temperature decreased below 110 °C and the consequent decomposition of hydrogen peroxide leading to a runaway would take place. An increase of the reaction enthalpy had a more significant impact on the position of the proposed operating points. In case of a reaction enthalpy increase by only 5 %, all but one (F) operating points were shifted to the hazardous operating regime because of the temperature in the reactor exceeded the upper safety constraint of 125 °C, which leads to possible over-pressurization of the reactor. If the reaction enthalpy was increased by 10 %, every proposed operating point was in the hazardous operating regime.

Conclusion Simulation-based approach for process intensification and hazard identification combination was proposed. As a case study, the production process of 3-methylpyridine-N-oxide was selected. First, a mathematical model suitable for scale-up and reaction conditions optimization of the CSTR for 3-methylpyridine-N-oxide production was developed in the MATLAB® modelling environment. In the next step, reaction conditions were optimized towards maximizing the production rate of 3-methylpyridine-N-oxide with process safety constraints’ implementation. Six different reactor operating points with the production rate increased by ca. 6 % were proposed based on process simulation and multi-parametric optimization. Consequent model parameter uncertainty analysis was performed. For the studied range of reaction enthalpy relative change by 5 % from the original value, only one from the proposed operating points was found to be satisfactory for safe operation. If the range of reaction enthalpy relative change was increased (relative change by 10 % from the original value), none of the proposed operating points was satisfactory for safe operation. This study has shown that an appropriate safety analysis is always required prior to the implementation of an intensified process. The need for model parameter uncertainty analysis in the simulation-based process intensification and hazard identification was underlined. In our future work, implementation of the presented procedures into smart software solution for supporting hazard identification techniques will be studied. Such software tool can be used to design inherently safer processes and also to train operators and process engineers in existing industrial plants.

Acknowledgment This work was supported by the Slovak Scientific Agency, Grant No. VEGA 1/0749/15 and the Slovak Research and Development Agency APP-14-0317 and the AXA Endowment Trust at the Pontis Foundation.

Literature 1. Kletz T. A.: Chem. Ind. 9124, 287 (1978). 2. Mannan S.: Lees’ Loss Prevention in the Process Industries: Hazard Identification, Assessment and

Control. Elsevier Science, Oxford 2012. 3. Sempere J., Nomen R., Rodriguez J. L., Papadaki M.: Chem. Eng. Process. 37, 33 (1998). 4. Pineda-Solano A., Saenz L. R., Carreto V., Papadaki M., Mannan S.: J. Loss Prev. Process Ind. 25, 797

(2012). 5. Pineda-Solano A., Saenz-Noval L., Nayak S., Waldram S., Papadaki M., Mannan S.: Process Saf. Environ.

Prot. 90, 404 (2012). 6. Cui X., Mannan S., Wilhite B. A.: Chem. Eng. Sci. 137, 487 (2015).

MULTILEVEL DATA ANALYSIS IN COMPUTER AIDED HAZARD IDENTIFICATION Janošovský J., Danko M., Labovský J., Jelemenský Ľ. Institute of Chemical and Environmental Engineering, Slovak University of Technology, Radlinského 9, 812 37 Bratislava, Slovakia ludovit.jelemensky@stuba.sk

Abstract Hazard identification techniques in chemical industry are constantly being improved by advanced computer simulations of chemical plants. Output from computer simulations is a large set of simulation data containing relevant information about individual streams and units involved in the simulated plant such as flow, temperature, pressure, composition, etc. This data is frequently used in process intensification activities but can be also exploited for process safety improvement. Software structure and simulation data analysis methods appropriate for computer aided hazard identification are discussed in this paper. The HAZOP (HAZard and OPerability study) methodology was adopted to generate process variables deviations and to evaluate their consequences via multilevel analysis comprising optimised numerical procedures as well as several graphical interpretations. Software features are previewed in application to two case studies. Presented approach allows investigating complex chemical processes from the safety engineering point of view in more depth and provides more effective analysis of complex fault propagation paths.

Introduction The constant growth of chemical industry led to the increase of manufacturing processes complexity to achieve higher product yields and purity at lower costs. Therefore, appropriate process safety analysis has become one of the most important aspects in plant design and operation. With the development of CAPE/PSE (computer aided production engineering/process systems engineering) tools, the demand for computer aided hazard identification has also increased. Several hazard identification techniques are well established in industrial companies’ policy such as What-If analysis, Checklist, FMEA (Failure modes and analysis) and HAZOP (HAZard and OPerability study)1,2. Structural and systematic approach of these techniques qualify them as potential candidates for computer aided hazard identification3,4. In our work, HAZOP principles were chosen to be implemented in software solution because of its robustness and wide application in chemical industry5. HAZOP methodology is based on generation of process variable deviations from design intent and analysis of their consequences. Process variable deviations are created by the combination of standardised guide words (No, More, Less, As Well As, Part Of, Reverse, and Other Than) and appropriate process variables (temperature, pressure, flow, level …)6. Although conventional HAZOP is considered as the most comprehensive hazard identification procedure, various drawbacks of this method have been noticed by experienced practitioners, e.g. uncommon hazards overlooking, significant time-consuming character, insufficient design intent definition, considerations of redundant deviations not leading to scenarios of concern, etc.7 Some conventional HAZOP drawbacks can be reduced or fully eliminated by implementing simulation-based approach. In this paper, a smart software system utilizing HAZOP principles and mathematical modelling of common chemical processes is introduced. The presented software was tested in combination with the simulation platform represented by Aspen HYSYS – a commercial process simulator widely used in chemical industry, particularly in oil and gas industry. The HAZOP methodology served as a tool for the generation of simulation inputs (HAZOP deviations). Severity of the simulated process states after HAZOP deviation occurrence (HAZOP consequences) was determined by multilevel simulation data analysis comprising optimised numerical procedures. Examples of software graphical user interface (GUI) are also provided.

Case studies Mathematical models of examined processes prepared in the corresponding simulation platform are necessary for computer aided hazard identification using the proposed software solution. For the demonstration of software application variability, two manufacturing processes employing different reactor types were analysed. Mathematical models of an ammonia synthesis reactor (Figure 1) and a glycerol nitration process (Figure 2) built in Aspen HYSYS were selected as case studies. Detailed overview of their model parameters and design intent conditions considered for HAZOP can be found in our previous works8,9. Implemented reaction kinetic models in both case studies were verified by experimental results and by comparison with data from industrial operation10,11.

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Figure 1. Model of ammonia synthesis reactor built in Aspen HYSYS environment

Figure 2. Model of glycerol nitration process built in Aspen HYSYS environment

Software application – simulation phase After the reliability and validity of mathematical models were confirmed, the proposed software tool could initiate the HAZOP analysis. At first, connection with the corresponding Aspen HYSYS case file was established and information about individual HAZOP nodes was accessed. The user can browse through three different types of HAZOP nodes: material streams, energy streams and unit operations (Figure 3). As shown, the software tool found six unit operations (Figure 3a) suitable for HAZOP analysis of the ammonia synthesis reactor (unit V-100 represented phase separator which text label in Aspen HYSYS flowsheet (Figure 1) was hidden) and seven material streams (Figure 3b) suitable for HAZOP analysis of the glycerol nitration process.

Figure 3. Example of HAZOP nodes’ parameters display in GUI of the proposed software tool for ammonia synthesis reactor (a) and glycerol nitration process (b) After successful access to Aspen HYSYS data, generation of HAZOP deviations was allowed. The user can apply logic guide words to any of the permitted process variables to create a list of HAZOP deviations. Example of the HAZOP deviation list for the ammonia synthesis reactor is provided in Figure 4 where HAZOP deviations “higher/lower pressure of fresh feed” and “higher/lower content of nitrogen in fresh feed” were created and stored. Currently, only the application of quantitative guide words is implemented. The correct use of qualitative guide words in computer aided approach is very limited because of their imprecise definition and therefore

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Figure 1. Model of ammonia synthesis reactor built in Aspen HYSYS environment

Figure 2. Model of glycerol nitration process built in Aspen HYSYS environment

Software application – simulation phase After the reliability and validity of mathematical models were confirmed, the proposed software tool could initiate the HAZOP analysis. At first, connection with the corresponding Aspen HYSYS case file was established and information about individual HAZOP nodes was accessed. The user can browse through three different types of HAZOP nodes: material streams, energy streams and unit operations (Figure 3). As shown, the software tool found six unit operations (Figure 3a) suitable for HAZOP analysis of the ammonia synthesis reactor (unit V-100 represented phase separator which text label in Aspen HYSYS flowsheet (Figure 1) was hidden) and seven material streams (Figure 3b) suitable for HAZOP analysis of the glycerol nitration process.

Figure 3. Example of HAZOP nodes’ parameters display in GUI of the proposed software tool for ammonia synthesis reactor (a) and glycerol nitration process (b) After successful access to Aspen HYSYS data, generation of HAZOP deviations was allowed. The user can apply logic guide words to any of the permitted process variables to create a list of HAZOP deviations. Example of the HAZOP deviation list for the ammonia synthesis reactor is provided in Figure 4 where HAZOP deviations “higher/lower pressure of fresh feed” and “higher/lower content of nitrogen in fresh feed” were created and stored. Currently, only the application of quantitative guide words is implemented. The correct use of qualitative guide words in computer aided approach is very limited because of their imprecise definition and therefore

practically infinite possibilities of their interpretation. In the next step, the user can assign a value range to selected HAZOP deviations. When the final HAZOP deviation list is completed, the proposed software tool proceeds into the process simulation phase. Stored HAZOP deviations are selected one-by-one and inserted to Aspen HYSYS. Once the simulated process correctly converged to a new state, configuration of the Aspen HYSYS simulation case file, i.e. HAZOP consequence, is assigned to the corresponding HAZOP deviation and stored for severity determination via multilevel simulation data analysis.

Figure 4. Example of HAZOP deviation list in GUI of the proposed software tool for ammonia synthesis reactor

Software application – data analysis Evaluation of HAZOP consequences’ severity is performed in a simulation data analysis module of the proposed software tool. It employs advanced numerical algorithms for the automated HAZOP analysis in optional combination with the analysis and monitoring of process- or unit-specific safety restrictions provided by the user. These two approaches are implemented for partial automation of the investigation procedure. When the investigation procedure is completed, the identified hazards and operability problems are assigned to a HAZOP consequence and stored. In GUI of the presented software tool, the user can browse through several types of analysis. An example of such analyses is depicted in Figures 5 and 6. Figure 5 represents the hazard identification procedure for a process exhibiting nonlinear behaviour with steady state multiplicity and Figure 6 represents the hazard identification procedure for a process exhibiting nonlinear behaviour without steady state multiplicity. The first type of analysis monitors the effect of the HAZOP deviation value on one parameter of one HAZOP node. This analysis consists of three supplementary methods – analysis of absolute parameter change from the design intent, analysis of relative parameter change from the design intent and parametric sensitivity analysis. Parametric sensitivity analysis allows capturing a sudden change of the process parameter that indicates e.g. the presence of steady state multiplicity in examined system. Figure 5a and Figure 6a show the difference in the parametric sensitivity analysis outputs for a system with and without steady state multiplicity. The second type of analysis monitors the effect of one HAZOP deviation value on selected parameters of one HAZOP node. The default mode of this type of analysis for graphical interpretation depicts relative change of selected parameters from the design intent (Figure 5b and Figure 6b). This analysis allows a more detailed overview of the overall response of one HAZOP node for particular HAZOP deviation and thus reduces the possibility of hazardous parameter change overlooking. The third possible analysis is focused on monitoring a change of one parameter of the selected HAZOP nodes for one HAZOP deviation value (Figure 5c and Figure 6c). This method provides in-depth analysis of the deviation propagation path through the examined system. Identified hazardous events and significant operability problems are formulated in a simplified HAZOP-like report that can serve as a preliminary analysis and supporting material for human expert HAZOP teams to detect complicated deviation propagation paths in modern complex production systems and thus to reduce time requirements of hazard identification in modern industrial manufactures.

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Figure 5 – Example of multilevel simulation data analysis in GUI of the proposed software tool for ammonia synthesis reactor (a – parametric sensitivity analysis, b – relative change of selected parameters for one HAZOP node, c – relative change of one parameter for selected HAZOP nodes)

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Figure 5 – Example of multilevel simulation data analysis in GUI of the proposed software tool for ammonia synthesis reactor (a – parametric sensitivity analysis, b – relative change of selected parameters for one HAZOP node, c – relative change of one parameter for selected HAZOP nodes)

Figure 6 – Example of multilevel simulation data analysis in GUI of the proposed software tool for glycerol nitration process (a – parametric sensitivity analysis, b – relative change of selected parameters for one HAZOP node, c – relative change of one parameter for selected HAZOP nodes)

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Conclusion Application of a smart software tool for automated model-based hazard identification in two case studies was presented. Aspen HYSYS was used as the simulation engine and HAZOP was used as the hazard identification method because of their frequent and successful application in chemical industry. The proposed simulation-based approach enables identification of process hazards and operability problems considering the HAZOP deviation size and represents an upgrade to the conventional HAZOP study where usually only the existence of a deviation is considered. A set of presented graphical interpretations of deviation propagation in systems with and without the presence of multiple steady states phenomena demonstrated the variability and robustness of the hazard identification procedure performed by the proposed software tool. The developed tool can be easily adapted for other chemical plants using the general modelling environment of Aspen HYSYS. Future research will be focused on the development of HAZOP consequences ranking system for more effective hazard assessment and on the proposal of a new simulation engine optimised for the purposes of hazard identification.

Acknowledgment This work was supported by the Slovak Scientific Agency, Grant No. VEGA 1/0749/15 and the Slovak Research and Development Agency APP-14-0317.

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Elsevier Science, Oxford 2012. 2. Occupational Safety and Health Administration: Process Safety Management (OSHA 3132). U.S.

Department of Labor, Washington 2000. 3. Dunjó J., Fthenakis V., Vílchez J. A., Arnaldos J.: J. Hazard. Mater. 173, 21 (2010). 4. Khan F., Rathnayaka S., Ahmed S.: Process Saf. Environ. Prot. 98, 116 (2015). 5. Crawley F., Tyler B.: HAZOP: Guide to Best Practice, 3rd edition. Elsevier Science, Oxford 2015. 6. Kletz T. A.: Reliab. Eng. Syst. Saf. 55, 263 (1997). 7. Baybutt, P.: J. Loss Prev. Process Ind. 33, 52 (2015). 8. Janošovský J., Labovský J., Jelemenský Ľ.: Acta Chim. Slovaca 8, 5 (2015). 9. Janošovský J., Danko M., Labovský J., Jelemenský Ľ.: Process Saf. Environ. Prot. 107, 12 (2017). 10. Morud J., Skogestad S.: AIChE J. 44, 889 (1998). 11. Lu K., Luo K., Yeh T., Lin P.: Process Saf. Environ. Prot. 86, 37 (2008).