Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Safety improvement in a gas refinery based on resilience engineering and macro-ergonomics indicators: a Bayesian network–artificial neural network approach

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

The risk of accidents at workplaces, particularly in the sensitive locations with unsafe behaviors, have increased substantially, needing to be managed accurately. To ameliorate the safety in such systems, enhancing the integrated resilience engineering and macro-ergonomics concepts is of pivotal importance. In this sense, this paper unveils a novel method based on Bayesian network and artificial neural network models to enhance safety of such systems considering both mentioned concepts. Exploiting the Bayesian network, the effects of the indicators on the system safety efficiency is evaluated according to the expert’s opinions. The Artificial neural network examines these effects based on the operator’s opinions. Thereinafter, to decrease the uncertainty and bias of results and also augment the robustness and accuracy of them, the combination of the results of these models is considered as the final criterion. For analyzing the efficacy of the proposed method, a case study in a gas refinery in Ilam, Iran is conducted. The results corroborate the validity and efficacy of the proposed method and draw outstanding managerial insights.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Abdallah J, Al-Thamier A (2004) The economic-environmental neural network model for electrical power dispatching. J Appl Sci 4(3):340–343

    Google Scholar 

  • Abolbashari MH et al (2018) Smart buyer: a Bayesian network modelling approach for measuring and improving procurement performance in organisations. Knowl Based Syst 142:127–148

    Google Scholar 

  • Aju Kumar V, Gandhi O (2011) Quantification of human error in maintenance using graph theory and matrix approach. Qual Reliab Eng Int 27(8):1145–1172

    Google Scholar 

  • Asadzadeh S et al (2013) Assessment and improvement of integrated HSE and macro-ergonomics factors by fuzzy cognitive maps: the case of a large gas refinery. J Loss Prev Process Ind 26(6):1015–1026

    Google Scholar 

  • Azadeh A, Zarrin M (2016) An intelligent framework for productivity assessment and analysis of human resource from resilience engineering, motivational factors, HSE and ergonomics perspectives. Saf Sci 89:55–71

    Google Scholar 

  • Azadeh A et al (2008) An integrated and multivariate model along with designing experiments approach for assessment of micro-and macro-ergonomic factors: the case of a gas refinery. J Res Health Sci 8(2):28–39

    MathSciNet  Google Scholar 

  • Azadeh A et al (2011) An adaptive neural network algorithm for assessment and improvement of job satisfaction with respect to HSE and ergonomics program: the case of a gas refinery. J Loss Prev Process Ind 24(4):361–370

    MathSciNet  Google Scholar 

  • Azadeh A et al (2013) A flexible intelligent algorithm for identification of optimum mix of demographic variables for integrated HSEE-ISO systems: the case of a gas transmission refinery. J Loss Prev Process Ind 26(6):1159–1182

    Google Scholar 

  • Azadeh A et al (2014a) Assessment of resilience engineering factors in high-risk environments by fuzzy cognitive maps: a petrochemical plant. Saf Sci 68:99–107

    Google Scholar 

  • Azadeh A, Gaeini Z, Moradi B (2014b) Optimization of HSE in maintenance activities by integration of continuous improvement cycle and fuzzy multivariate approach: a gas refinery. J Loss Prev Process Ind 32:415–427

    Google Scholar 

  • Azadeh A et al (2014c) Performance evaluation of integrated resilience engineering factors by data envelopment analysis: the case of a petrochemical plant. Process Saf Environ Prot 92(3):231–241

    Google Scholar 

  • Azadeh A et al (2016) A unique adaptive neuro fuzzy inference system for optimum decision making process in a natural gas transmission unit. J Nat Gas Sci Eng 34:472–485

    Google Scholar 

  • Azadeh A et al (2017a) Combinatorial optimization of resilience engineering and organizational factors in a gas refinery by a unique mathematical programming approach. Hum Factors Ergon Manuf Serv Ind 27(1):53–65

    Google Scholar 

  • Azadeh A, Roudi E, Salehi V (2017b) Optimum design approach based on integrated macro-ergonomics and resilience engineering in a tile and ceramic factory. Saf Sci 96:62–74

    Google Scholar 

  • Bezdek JC (1973) Fuzzy mathematics in pattern classification. Cornell University, Ithaca

    Google Scholar 

  • Campos LM (2006) A scoring function for learning Bayesian networks based on mutual information and conditional independence tests. J Mach Learn Res 7:2149–2187

    MathSciNet  MATH  Google Scholar 

  • Cang S, Yu H (2014) A combination selection algorithm on forecasting. Eur J Oper Res 234(1):127–139

    MathSciNet  MATH  Google Scholar 

  • Clemen RT (1989) Combining forecasts: a review and annotated bibliography. Int J Forecast 5(4):559–583

    Google Scholar 

  • Cooper GF, Herskovits E (1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9(4):309–347

    MATH  Google Scholar 

  • Cooper DR, Schindler PS, Sun J (2003) Business research methods. McGraw-Hill Irwin, Boston

    Google Scholar 

  • Cronbach LJ (1951) Coefficient alpha and the internal structure of tests. Psychometrika 16(3):297–334

    MATH  Google Scholar 

  • De Hoon MJ et al (2002) Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations. In: Biocomputing 2003. World Scientific, pp 17–28

  • Dıez FJ, Druzdzel MJ (2006) Canonical probabilistic models for knowledge engineering. Technical Report CISIAD-06-01, UNED, Madrid, Spain

  • Elidan G, Nachman I, Friedman N (2007) “Ideal Parent” structure learning for continuous variable Bayesian networks. J Mach Learn Res 8(8):1799–1833

    MathSciNet  MATH  Google Scholar 

  • Fam IM, Nikoomaram H, Soltanian A (2012) Comparative analysis of creative and classic training methods in health, safety and environment (HSE) participation improvement. J Loss Prev Process Ind 25(2):250–253

    Google Scholar 

  • Fethi MD, Pasiouras F (2010) Assessing bank efficiency and performance with operational research and artificial intelligence techniques: a survey. Eur J Oper Res 204(2):189–198

    MATH  Google Scholar 

  • García-Alonso CR, Torres-Jiménez M, Hervás-Martínez C (2010) Income prediction in the agrarian sector using product unit neural networks. Eur J Oper Res 204(2):355–365

    MATH  Google Scholar 

  • Garvey MD, Carnovale S, Yeniyurt S (2015) An analytical framework for supply network risk propagation: a Bayesian network approach. Eur J Oper Res 243(2):618–627

    MathSciNet  MATH  Google Scholar 

  • Good IJ (1961) A causal calculus (I). Br J Philos Sci 11(44):305–318

    MathSciNet  Google Scholar 

  • Hallock M, Alper S, Karsh B (2006) A macro-ergonomic work system analysis of the diagnostic testing process in an outpatient health care facility for process improvement and patient safety. Ergonomics 49(5–6):544–566

    Google Scholar 

  • Hansson L et al (2009) Applying the resilience concept in practice: a case study from the oil and gas industry. In: Martorell et al (eds) Safety, reliability and risk analysis: theory, methods and applications, vol 4. Taylor & Francis Group, London

    Google Scholar 

  • Hegde S et al (2014) Towards the development of a resilience engineering tool to improve patient safety: the RETIPS approach. In: Proceedings of the human factors and ergonomics society annual meeting. SAGE Publications, Los Angeles

  • Hendrick HW (1995) Humanizing re-engineering for true organizational effectiveness: a macroergonomic approach. In: Proceedings of the human factors and ergonomics society annual meeting. SAGE Publications, Los Angeles

  • Hsu SH et al (2010) The influence of organizational factors on safety in Taiwanese high-risk industries. J Loss Prev Process Ind 23(5):646–653

    Google Scholar 

  • Huber S et al (2009) Learning from organizational incidents: resilience engineering for high-risk process environments. Process Saf Prog 28(1):90–95

    Google Scholar 

  • Izakian H, Abraham A (2011) Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Syst Appl 38(3):1835–1838

    Google Scholar 

  • Jafarzadeh-Ghoushchi S (2015) Optimization of transportation system based on combined model using artificial neural networks and response surface methodology. Int J Tech Rev Appl 23:69–76

    Google Scholar 

  • Jeffcott S, Ibrahim J, Cameron P (2009) Resilience in healthcare and clinical handover. BMJ Qual Saf 18(4):256–260

    Google Scholar 

  • Jiang P, Liu X (2016) Hidden Markov model for municipal waste generation forecasting under uncertainties. Eur J Oper Res 250(2):639–651

    MATH  Google Scholar 

  • Khakzad N, Reniers G, Landucci G (2017) Application of Bayesian network to safety assessment of chemical plants during fire-induced domino effects. In: 26th European safety and reliability conference, ESREL 2016. CRC Press/Balkema

  • Khandan M, Vosoughi S, Maghsoudipour M (2012) Evaluation of safety climate factors-a macroergonomics approach: a case study in Iran. Iran Rehabil J 10:43–46

    Google Scholar 

  • Kleiner BM (1999) Macroergonomic analysis and design for improved safety and quality performance. Int J Occup Saf Ergon 5(2):217–245

    Google Scholar 

  • Kumar PR, Ravi V (2007) Bankruptcy prediction in banks and firms via statistical and intelligent techniques: a review. Eur J Oper Res 180(1):1–28

    MATH  Google Scholar 

  • Lacher RC et al (1995) A neural network for classifying the financial health of a firm. Eur J Oper Res 85(1):53–65

    MATH  Google Scholar 

  • Lee K (2005) Ergonomics in total quality management: how can we sell ergonomics to management? Ergonomics 48(5):547–558

    Google Scholar 

  • Leu S-S, Chang C-M (2013) Bayesian-network-based safety risk assessment for steel construction projects. Accid Anal Prev 54:122–133

    Google Scholar 

  • Leveson NG (2002) System safety engineering: back to the future. Massachusetts Institute of Technology

  • Li G et al (2017) A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base. Saf Sci 93:108–120

    Google Scholar 

  • Li H et al (2019) The updating strategy for the safe control Bayesian network model under the abnormity in the thickening process of gold hydrometallurgy. Neurocomputing 338:237–248

    Google Scholar 

  • Luczak H (1995) Macroergonomic anticipatory evaluation of work organization in production systems. Ergonomics 38(8):1571–1599

    Google Scholar 

  • Miriyala SS, Subramanian V, Mitra K (2017) TRANSFORM-ANN for online optimization of complex industrial processes: casting process as case study. Eur J Oper Res 264:294–309

    MathSciNet  MATH  Google Scholar 

  • Misiunas N et al (2016) DEANN: a healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status. Omega 58:46–54

    Google Scholar 

  • Mohammadfam I et al (2017) Constructing a Bayesian network model for improving safety behavior of employees at workplaces. Appl Ergon 58:35–47

    Google Scholar 

  • Morel G, Amalberti R, Chauvin C (2009) How good micro/macro ergonomics may improve resilience, but not necessarily safety. Saf Sci 47(2):285–294

    Google Scholar 

  • Neapolitan RE (2004) Learning Bayesian networks, vol 38. Pearson Prentice Hall, Upper Saddle River

    Google Scholar 

  • Nie S, de Campos CP, Ji Q (2017) Efficient learning of Bayesian networks with bounded tree-width. Int J Approx Reason 80:412–427

    MathSciNet  MATH  Google Scholar 

  • Nielsen TD, Jensen FV (2009) Bayesian networks and decision graphs. Springer, Berlin

    MATH  Google Scholar 

  • Parviainen P, Kaski S (2017) Learning structures of Bayesian networks for variable groups. Int J Approx Reason 88:110–127

    MathSciNet  MATH  Google Scholar 

  • Pearl J (2014) Probabilistic reasoning in intelligent systems: networks of plausible inference. Elsevier, Amsterdam

    MATH  Google Scholar 

  • Ross A et al (2014) Inpatient diabetes care: complexity, resilience and quality of care. Cognit Technol Work 16(1):91–102

    Google Scholar 

  • Roudneshin M, Azadeh A (2019) A novel multi-objective fuzzy model for optimization of oil sludge management by considering Health, Safety and Environment (HSE) and resiliency indicators in a gas refinery. J Clean Prod 206:559–571

    Google Scholar 

  • Russell S, Norvig P (2016) Artificial intelligence: a modern approach. Pearson, Upper Saddle River

    MATH  Google Scholar 

  • Sermpinis G et al (2017) European exchange trading funds trading with locally weighted support vector regression. Eur J Oper Res 258(1):372–384

    MathSciNet  MATH  Google Scholar 

  • Shirali GA et al (2016) Assessment of resilience engineering factors based on system properties in a process industry. Cognit Technol Work 18(1):19–31

    MathSciNet  Google Scholar 

  • Wreathall J (2006) Properties of resilient organizations: an initial view. Resilience engineering: concepts and precepts, pp 275–285

  • Zhang G et al (1999) Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. Eur J Oper Res 116(1):16–32

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Ali Taghi-Molla & Masoud Rabbani

  2. Department of Industrial Engineering and Management, Malek Ashtar University of Technology, Tehran, Iran

    Mohammad Hosein Karimi Gavareshki

  3. School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

    Ehsan Dehghani

  4. National Elites Foundation of Iran, Tehran, Iran

    Ehsan Dehghani

Authors
  1. Ali Taghi-Molla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Masoud Rabbani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Hosein Karimi Gavareshki
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ehsan Dehghani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Taghi-Molla.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Taghi-Molla, A., Rabbani, M., Karimi Gavareshki, M.H. et al. Safety improvement in a gas refinery based on resilience engineering and macro-ergonomics indicators: a Bayesian network–artificial neural network approach. Int J Syst Assur Eng Manag 11, 641–654 (2020). https://doi.org/10.1007/s13198-020-00968-x

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-020-00968-x

Keywords

Search

Navigation