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Droguett, Enrique López, Ph.D.

Associate Professor
Mechanical Engineering
Center for Risk and Reliability
Hybrid-System Integration and Simulation Lab
0151D Glenn L. Martin Hall, Building 088
Email: 
Phone: 
301-314-1036

Research Interests 

  • Reliability Engineering with applications to Design and Manufacturing with special interest in Energy sector (particularly the Oil and Gas Industry and Hydropower), Mining sector, and Healthcare systems:
    • Prognostics and System Health Management
    • System Reliability and Maintenance Modeling and Optimization
    • Bayesian Methods (Inference with Uncertain Evidence, Use of Expert Opinion, Causal Modeling with Bayesian Belief Networks)
    • Development of procedures for reliability and uncertainty analysis of complex systems based on Non-Extensive Entropy (in particular, Tsallis Entropy)
  • Physical Asset Management in the Energy, Healthcare, and Mining sectors
  • Numerical Techniques and Simulation models for Reliability and Risk Analysis of Complex Systems
  • Risk Analysis:
    • Uncertainty Modeling and Analysis
    • Prediction of Extreme Events Related to Climate Change
    • Human Reliability Modeling Based on Neurophsychophysiological Methods and Use of Virtual Reality
    • Ecological Risk Analysis
    • Biological Risk Analysis

Background 

Enrique López Droguett joined the University of Maryland, College Park, in 2014. He conducts research on methods for probabilistic risk analysis and reli­ability of systems, uncertainty analysis, Bayesian methods, maintenance optimization, and ecologi­cal and biological risk assessment. He has led many major studies on risk and reliability of complex systems such as oil and gas exploration and production, oil refineries, commercial aviation, and hydropower plants. He has over 120 papers in refereed journals and proceedings of conferences and two books in various areas of risk, reliability engineering and maintenance.

Education 

  • Ph.D., University of Maryland, College Park, 1999

Honors and Awards 

  • Society for Risk Analysis (SRA) Student Merit Award winner in the Ecological Risk Assessment Specialty Group for the paper "Quantitative Ecological Risk Assessment of Industrial Accidents: The Case of Oil Ship Transportation in Coastal Tropical Area of Northeastern Brazil", with PhD Student Heitor Duarte (2012)
  • Patron Professor Honor by the Production Engineering Undergraduate Class of 2005
  • Honored Professor by the Production Engineering Undergraduate Class of 2003
  • Honored Professor by the Production Engineering Undergraduate Class of 2004
  • Honored Professor by the Production Engineering Undergraduate Class of 2007
  • NASA Group Achievement Award (1998)
  • NASA Letter of Recognition for Technical Excellence (1998)

Professional Memberships 

  • Society for Risk Analysis
  • IEEE Society
  • Society of Petroleum Engineers
  • European Safety and Reliability Association (as president of ABRISCO, the Brazilian Risk Analysis, Safety and Reliability Association)
  • Brazilian Society of Operations Research
  • Brazilian Association of Industrial Engineering
  • Brazilian Association for Risk Analysis, Process Safety and Reliability (ABRISCO)

Selected Publications 

Books

  1. Moura, M.C.; Droguett, E.L. “Novel and Faster Ways for Solving Semi-Markov Processes: Mathematical and Numerical Issues”. 1 ed. Saarbrücken, Germany: LAP - Lambert Academic Publishing, 2010.
  2. Lins, I.D.; Moura, M.C.; Droguett, E.L. “Support Vector Machines and Particle Swarm Optimization: Applications to Reliability Prediction”. 1 ed. Saarbrücken, Germany: LAP - Lambert Academic Publishing, 2010.

Journal Papers

  1. Pascual, R.; Madariaga, R.; Santelices, G.; Godoy, D.; Droguett, E.L. “A Structured Methodology to Optimize Throughput of Production Lines.” International Journal of Mining, Reclamation and Environment, (accepted), 2014.
  2. Moura, M. C.; Lins, I. D.; Droguett, E.L.; Soares, R.; Pascual, R. “A Multi-Objective Genetic Algorithm for Determining Efficient Risk-Based Inspection Programs.” Reliability Engineering & Systems Safety, (accepted), 2014.
  3. Droguett, E.L; Lins, I.D; Moura, M.C; Zio, E.; Jacinto, C.M.C. “Variable Selection and Uncertainty Analysis of Scale Growth Rate under Pre-Salt Oil Wells Conditions using Support Vector Regression.” Journal of Risk and Reliability (accepted), 2014.
  4. Martins, M.R.; Schleder, A.; Droguett, E.L. “A Methodology for Risk Analysis Based on Hybrid Bayesian Networks - Application to the Regasification System of Liquefied Natural Gas Onboard of a Floating Storage and Regasification Unit.” Risk Analysis (accepted), 2014.
  5. Moura, M.C.; Droguett, E.L.; Ferreira, R.; Firmino, P.R.A. “A Competing Risk Model for Dependent and Imperfect Condition-Based Preventive and Corrective Maintenances.” Journal of Risk and Reliability (accepted), 2014.
  6. Droguett, E.L.; Mosleh, A. “Bayesian Treatment of Model Uncertainty for Partially Applicable Models.” Risk Analysis, v.34, p. 252-270, 2014.
  7. Duarte, H.; Droguett, E.L.; Moura, M. C.; Barbosa, C.; Gomes, E.; Barbosa, V.; Araujo, M. “An Ecological Model for Quantitative Risk Assessment for Schistosomiasis: the Case of a Patchy Environment in the Coastal Tropical Area of Northeastern Brazil.” Risk Analysis, v.34, p.831-846, 2014.
  8. Leite, F.S.; Silva, M.A.; Araujo, M.; Silva, R.A.; Droguett, E.L. “Modeling Subsurface Gas Release in Tropical and Shallow Waters: Comparison with Field Experiments off Brazil's Northeast Coast.”Human and Ecological Risk Assessment, v.20, p.150-173, 2014.
  9. Droguett, E.L.; Mosleh, A. “Integrated treatment of model and parameter uncertainties through a Bayesian approach.” Journal of Risk and Reliability, v. 227, p. 41-54, 2013.
  10. Lins, I.D.; Moura, M.C.; Droguett, E.L.; Zio, E.; Jacinto, C.M. “Failure Prediction of Oil Wells by Support Vector Regression with Variable Selection, Hyperparameter Tuning and Uncertainty Analysis”. Chemical Engineering Transactions, v. 33, p. 817-822, 2013.
  11. Lins, I.D.; Rego, L.C.; Moura, M.C.; Droguett, E.L. “Selection of security system design via games of imperfect information and multi-objective genetic algorithm.” Reliability Engineering & Systems Safety, v. 112, p. 59-66, 2013.
  12. Lins, I.D.; Araujo, M.; Moura, M.C.; Silva, M.A.; Droguett, E.L. “Prediction of sea surface temperature in the tropical Atlantic by support vector machines.” Computational Statistics & Data Analysis, 2013.
  13. Duarte, H.O.; Droguett, E.L; Araújo, M.; Teixeira, S.F. “Quantitative Ecological Risk Assessment of Industrial Accidents: The Case of Oil Ship Transportation in the Coastal Tropical Area of Northeastern Brazil.” Human and Ecological Risk Assessment, v.19, p.1457-1476, 2013.
  14. Ak, R.; Li, Y.; Vitelli, V.; Zio, E.; Droguett, E.L.; Jacinto, C.M.  “NSGA-II-trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment.” Expert Systems with Applications, v.40, p.1205-1212, 2013.
  15. Droguett, E.L; Mosleh, A. “Integrated treatment of model and parameter uncertainties through a Bayesian approach.” Journal of Risk and Reliability, v.227, p.41-54, 2013.
  16. Lins, I.D.; Moura, M.C.; Droguett, E.L.; Zio, E.  “A particle swarm-optimized support vector machine for reliability prediction.” Quality and Reliability Engineering International, v. 28, p. 141-158, 2012.
  17. Leite, F.S.; Araujo, M.; Silva, M.; Silva, R.; Tyaquica, P.; Droguett, E.L. “Field Study of a Simulated Subsurface Gas Blowout in Tropical and Shallow Water along the Brazilian Coast.” Tropical Oceanography, v. 40, p. 240-261, 2012.
  18. Baraldi, P.; Di Maio, F.; Zio, E.; Sauco, S. ; Droguett, E.L.; Jacinto, C.M. “Sensitivity Analysis of Scale Deposition on Equipment of Oil Wells Plants.” Chemical Engineering Transactions, v. 26, p. 327-332, 2012.
  19. Moura, M.C; Lins, I.D.; Droguett, E.L.; Zio, E. “Failure and reliability prediction by support vector machines regression of time series data.” Reliability Engineering & Systems Safety, v. 96, p. 1527-1534, 2011.
  20. Lins, I.D.; Droguett, E.L. “Redundancy allocation problems considering systems with imperfect repairs using multi-objective genetic algorithms and discrete event simulation.” Simulation Modelling Practice and Theory, v. 19, p. 362-381, 2011.