791 • Integrated results improve lifecycle costing through a detailed understanding of downtime drivers and maintenance requirements, enabling more accurate long-term cost assessments. Equipment sizing is also refined, helping engineers avoid over- or under-design by accurately forecasting operational loads and extreme scenarios. • Dynamic models allow control strategies and safety instrumented systems to be tested and refined well before construction or modification, reducing commissioning risks and ensuring a more reliable and robust control environment. • Ultimately, integrating DPS and RAM shifts decision-making from static reliability predictions to a dynamic, cause-and-effect understanding of system behaviour—supporting safer, more efficient, and more reliable operations. By integrating RAM and DPS, engineers move from a static reliability prediction to a dynamic, cause-and-effect understanding of system performance, leading to safer, more efficient, cost efficient, and more reliable operations REFERENCES Prashanth Parthasarathi, Virgil Szaruga and Marian Szatkowski., 2009, Benefits of Dynamic Process Simulation for Minerals Industry, Andritz Automation Report. Salazar, J.L., Magne, L., Acuna, G. and Cubillos, F. 2009, Dynamic modeling and simulation of semiautogenous mills. Minerals Engineering. 22: 70 C. Cordier, M. Fayot, A. Leroy, A. Petit. 1997, Integration of process simulations in availability studies. Reliability Engineering & System Safety, Volume 55, Issue 2, February 1997, Pages 105-116 Cremonese, D.T, Karanth, B, De Tomi, G. 2017, A high-level dynamic analysis approach for studying global process plant availability and production time in the early stages of mining projects. REM, Int. Eng. J. 70 (2) • Apr-Jun 2017 Brett W. Schug, Michael R. Nees, Thomas V. Gamarano., 2012, Process Simulation for Improved Plant Design, Engineering, Environmental Science.
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