349532 Thinking Like Sherlock Holmes for Process Filtration Technology Selection

Monday, March 31, 2014: 1:30 PM
Prince of Wales (Hilton New Orleans Riverside)
Barry A. Perlmutter, BHS-Filtration Inc., Charlotte, NC

Sherlock Holmes and Dr. John Watson are fictional characters of Sir Arthur Conan Doyle.  Process engineers who live in the real-world can learn many things from the two of them for solving process filtration problems.  This paper will intertwine the detective techniques (mindfulness, astute observation, logical deduction and others) of Holmes and Watson with the problem solving skills required to select process filtration systems. 

One example that Holmes proves time and again is that there is no benefit to jumping to conclusions.  The paper begins with a discussion of the bench-top laboratory tests that are conducted for problem analysis, technology selection and scale-up.  The tests include pressure/vacuum/centrifugation, filter media, cake thickness, temperature and viscosity concerns, filter aids and similar process parameters. 

Another technique used by Holmes and Watson is “recreating events.”   Holmes talks through his theories out loud to Watson and only then do gaps and inconsistencies rise to the surface that were not apparent before.  The paper continues with four case history examples discussing slurry testing, process analysis and then process filtration selection for pressure filtration, vacuum filtration, centrifugation and clarification.   

Finally, the paper concludes with a general review of the problem-solving skills of Holmes and Watson such as the “occasional silence”, “employing distancing” and “learning to tell the crucial from the incidental.”  These skills can be utilized by process engineers as a framework for “idea-generation” when analyzing an operating bottleneck issue or new process development problem.  In all cases, by combining Holmes and Watson with accurate lab and pilot testing, the optimum filter selection can be realized.


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See more of this Session: Challenges in Process Scale-Up
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