The following tip is from the ISA book by Greg McMillan and Hunter Vegas titled 101 Tips for a Successful Automation Career, inspired by the ISA Mentor Program. This is Tip #61, and was written by Greg.

I never have quite understood why every raw material, utility, vent, reactant, recycle, and reagent flow rate and total is not ratioed to the product flow rate and total for each unit operation. The operator screens should display flow ratios (e.g., kg/kg or lb/lb), cost ratios (e.g., Euros/kg or $/lb), production rate ratios (e.g., kg/hr or lb/hr), and profit ratios (e.g., Euros/hr or $/hr). I have heard that major ethanol and pet food manufacturers do this, and have seen an increase in competitiveness between plants and shifts. I have seen energy ratios for boilers and kilns (e.g., kJ/kg or BTU/lb) but not much in terms of ratios for chemical and pharmaceutical plants.

Knowing how process performance changes with changes in operators, maintenance, and process technical support may be disconcerting at first but ultimately productive, possibly spurring competition between operators, engineers, technicians, and plants to do better. Here the old adage applies, “You cannot control something you don’t measure.” In order to improve the performance of each unit operation, you need to measure the performance of each unit operation. As with anything measured, automatic control is better than manual control. A linear program (LP) in model predictive control (MPC) can do automatic optimization of the metrics by the use of cost ratios and profit to find the optimum intersection of operating conditions.

Nearly every process input that is set by operators or automatically manipulated by controllers is a flow. There should be a measurement of every flow for process analysis besides metrics. We are accustomed to flow measurements of raw material, reactant, and product streams, but wireless transmitters and insertion-type flowmeters (e.g., annubars) make flow measurement affordable for the remaining streams.

If we have all the flows, we can do a material balance and implement plantwide feedforward control (Tip #101). We can correct for pressure disturbances and valve nonlinearities by flow control, making the job of PID control (and particularly model predictive control) much easier. Because process gain is a nonlinear function of the ratio of manipulated flow to total flow, the process time constant is proportional to residence time, and transportation delay is inversely proportional to flow rate, we can intelligently schedule tuning settings.

We can add wireless temperature measurements and do energy balance, heat release, and heat transfer calculations. We can develop inferential measurements of concentrations using neural networks, projections to latent structures, and first principles. We can improve the fidelity of a virtual plant. Since we have flow control, we do not need to have a pressure flow-solver in the virtual plant to know the flow through valves, and can adapt model parameters based on flow ratios (Tip #98 and Tip #99).

Concept: Operating efficiency can be computed from a ratio of flows and assigning dollars per unit flow. Online metrics open the door to process understanding and innovations by the quantification of benefits and first principle relationships.

Details: For continuous processes, compute the increase in production flow or onstream time or the reduction in the ratio of a utility, raw material, recycle, or reagent flow to a production flow. You should compute the ratios on a filtered instantaneous basis and as a ratio of totals for a representative period such as a shift. For batch processes, compute the ratios as flow totals to product total per batch and estimate the batch cycle time to get a production rate. For batch processes, efficiency is increased by higher batch end point concentration or lower total utility use and raw material feed per batch.

Batch process capacity is increased by a shorter cycle time. Use ballpark estimates of dollars per unit mass (e.g., lb or kg) and normal production rates to get to the bottom line ($/hr or $/batch). Coriolis meters provide the ultimate in terms of accuracy of flow and density measurements and two-component composition measurements (Tip #73). Total heat release measurements can provide an inferential measurement of reaction conversion and the heat release rate can provide an indication of conversion rate and batch completion.

Watch-outs: Signal filters may be needed to reduce the noise in flow and cost ratios. To synchronize an upstream flow or utility flow with a downstream product flow, flows may have to be passed through a deadtime block and filter block to simulate transportation delays and residence time lags. There may be an inverse response and a temporary decrease in efficiency from the action of an LP and MPC that can cause impatient operations personnel to think the advanced process control (APC) system is doing the wrong thing. For example, if a reactant feed is increased to be closer to the optimum stoichiometric ratio, the yield would decrease if the change in reactant flow is not delayed and lagged to match the change in measured product concentration and flow out of the reactor.

Exceptions: The synchronization of raw material flows with final product flows after many batch and continuous processes may not be possible, causing metrics to be erratic. Synchronization is particularly difficult when there are several unit operations between a flow being manipulated and a product flow being measured for optimization.

Insight: Process metrics depend upon flow measurements.

Rule of thumb: Add flow measurements to every important stream and compute online metrics for the process efficiency and capacity of each key unit operation.

About the Author
Gregory K. McMillan, CAP, is a retired Senior Fellow from Solutia/Monsanto where he worked in engineering technology on process control improvement. Greg was also an affiliate professor for Washington University in Saint Louis. Greg is an ISA Fellow and received the ISA Kermit Fischer Environmental Award for pH control in 1991, the Control magazine Engineer of the Year award for the process industry in 1994, was inducted into the Control magazine Process Automation Hall of Fame in 2001, was honored by InTech magazine in 2003 as one of the most influential innovators in automation, and received the ISA Life Achievement Award in 2010. Greg is the author of numerous books on process control, including Advances in Reactor Measurement and Control and Essentials of Modern Measurements and Final Elements in the Process Industry. Greg has been the monthly "Control Talk" columnist for Control magazine since 2002. Presently, Greg is a part time modeling and control consultant in Technology for Process Simulation for Emerson Automation Solutions specializing in the use of the virtual plant for exploring new opportunities. He spends most of his time writing, teaching and leading the ISA Mentor Program he founded in 2011.

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About the Author
Hunter Vegas, P.E., has worked as an instrument engineer, production engineer, instrumentation group leader, principal automation engineer, and unit production manager. In 2001, he entered the systems integration industry and is currently working for Wunderlich-Malec as an engineering project manager in Kernersville, N.C. Hunter has executed thousands of instrumentation and control projects over his career, with budgets ranging from a few thousand to millions of dollars. He is proficient in field instrumentation sizing and selection, safety interlock design, electrical design, advanced control strategy, and numerous control system hardware and software platforms. Hunter earned a B.S.E.E. degree from Tulane University and an M.B.A. from Wake Forest University.

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