The first document you have on a project is typically a process flow diagram (PFD). The PFD defines the process. It is the ultimate source of information and sets the plant performance and design. It is interesting to me that we really don't know how well existing operations match the PFD. When I have posed the question of what is really the mass flow, pressure, temperature, and density of a plant stream to university students and even seasoned engineers in industry, the usual reply is that the stream conditions are what is shown in the PFD. As humans we are naturally optimistic and want to think everything is as believed and stated. As engineers we are accustomed to numbers being accurate to several significant figures. Alas, if you had the knowledge of what is really going in the process there would be a rude awakening. While uncomfortable, the awareness leads to better process control improvements.

In the PFD and in chemical engineering courses, the plant is assumed to be at steady state. Of course this does not work for batch processes. Less obvious is that it doesn't work well for continuous processes with merging and diverging trains of equipment and recycle streams. Even if a plant was at steady state, I doubt it would be within 10% of the PFD design conditions on all of the PFD process variables due to non ideal and unknown effects in the process calculations or simulations that generated the PFD. Maybe things have changed a lot, but in my days working at a large chemical company, the process engineers manually updated personalized spreadsheets that attempted to close the material and energy balances (unless we are talking about nuclear reactions, energy and mass are conserved - neither created or destroyed).

What if a plant had a live online PFD? What if we had live online material and energy balances? What if we had temperature, pressure, mass flow, and inferential measurements of the composition in every important process stream?

Coriolis flowmeters offer a true mass flow measurement that does not depend upon composition, density, velocity profile, Reynolds number, or viscosity. The physics of the measurement afford a rangeability and accuracy that is unexcelled (for an excellent perspective see the article by Peter Ginn "Tt's the Physics!", InTech, Feb 1996). Coriolis also provides a direct density measurement, a tube temperature measurement, and when coupled with an accurate differential pressure transmitter (DP) for viscous fluids, an inferential viscosity measurement. In the last couple of years, major improvements have been made in Coriolis technology. For example, Coriolis meters can measure two phase flow and can infer void fraction. Meter sizes can be as small as 2 millimeters making them ideal for labs and pilot plants. For slurries and clingy sticky fluids, straight tubes and higher velocities can be used to prevent coatings and accumulation of material. Coriolis meters can potentially provide more accurate batch charges than weigh tanks because Coriolis meters retain a better long term installed accuracy than load cells since Coriolis does not suffer from drift or installation effects. For more information on Coriolis see the EssentialBookCoriolisExcerpt.pdf.pdf from the new ISA book Essentials of Modern Measurements and Final Elements

When a Coriolis meter is put on a stream, the only process variable missing for a live online PFD is pressure, which could be easily added via a wireless pressure transmitter. For streams with acids and bases, wireless conductivity and pH transmitters could provide additional information on stream composition. For example, in absorbers for CO2 capture, wireless pH and conductivity measurements in concert with a Coriolis density and temperature measurement can provide inferential measurements of solvent concentration and CO2 loading important for optimizing absorber flow distribution.

There is a lot of talk about online process metrics but as far as I can see, what is done is loop metrics principally on process variability. A live PFD would enable online process efficiency metrics (e.g. yield) for each unit operation besides tighter mass balances. The stream variables would also lead to better data analytics and prediction of product quality.

The opportunity afforded by online metrics is worth summarizing in this series even though it has been discussed in several entries on this website.

The need to cut costs has translated to an increased emphasis on process efficiency and the ability to justify software, hardware, and personnel. Increasingly these need to be hard benefits (e.g. reduction in raw material, downtime, and energy costs).

When I worked in process control improvement (PCI) in the technology department of a large chemical company, we had to show new benefits each year that were at least twice our salary to justify our job. By the end of the five year process control improvement effort we had 75 million dollars per year in savings documented. The PCI core group had 5 modeling and control specialists working with 20 or more process control engineers at key plants. The benefits reported depended upon the skills of particularly one person Glenn Mertz) who was extremely proficient in cost sheet analysis and working with operations and process technology.

Some companies are fortunate enough to have PCI as part of their culture as seen in the Control Talk Columns "Going, Going, Gone - Part 2" (September) and Part 3 (October) for examples. For many companies, benefits need to be reported in order for PCI and our profession to move forward or even exist. See the December 1 and 5, 2008 entries on this website "Past, Present, and Future of Automation - Part 5 (Benchmarking and Opportunity Assessment)" and Part 6 (Operator Interface) and the December 28, 2007 entry "Biggest Opportunities in Process Control Improvement - The Operator (Online Metrics) for more discussion of the aspects and importance of identifying and showing PCI benefits.

There are a lot of initiatives in the plant to improve plant operation by better operating procedures, equipment, and maintenance. All of these people take great pride in their work and are naturally eager to attribute better process operation to their efforts. Process technology often has the last say. The best way for PCI to get credit for improvement in plant operation is for the improvement and change to be visible in the data historian. A visible change in capacity, efficiency, or quality after a change in the process control system provides the documentation needed. If the PCI could be turned on and off, the correlation would be irrefutable but this is usually not practical. If no other events occurred when the PCI went online, a beginning of improved plant operation coinciding with the completion of the PCI, and a good explanation of cause and effect, will normally suffice for PCI to get credit. To help guide management and operations, comments should be entered in the historian and event makers for PCI provided.

PCI metrics for continuous process capacity are generally available from product flow measurements, downtime due to trips, and the time to startup or make a product grade transitions. PCI metrics for batch process capability can be generated from batch size, end point concentration, batch cycle time, and time in between batches. Quick and dramatic improvements in batch capacity have been achieved be the elimination of operator attention requests, manual actions, trips, and wait times for resource allocation (e.g. utility or charge systems), lab results, and reaction completion. Model predictive control and override control applications have been very successful for fed-batch processes. Reductions of 25% or more in batch cycle time are common for PCI. For a summary of some of the many possible batch control opportunities see BatchCycleTimeReduction.pdf from my PCI days.

PCI metrics for process efficiency are best expressed as a ratio of kilogram (pound) of input used (e.g. feed, fuel, reagent, and utility) per kilogram (pound) of product produced. For fuels, the numerator in the ratio may be expressed in thermal units, such as kilojoules (BTUs). For batch processes, the totalized input flow is divided by the batch size multiplied by the fractional product end point concentration. For continuous processes the instantaneous input flows are divided by intermediate or final product flow multiplied by the fractional product concentration. Synchronization of input flows to output flows can be done by the addition of a time constant equal to residence time and a time delay equal to the transportation delay. The flows can be totalized to compare shifts and periods of operation. Online process efficiency measurements require online or at-line analyzers or inferential measurements from first principle, neural network, polynomial, or statistical (e.g. PLS) models. These models in turn require flow measurements because nonlinear valve characteristics, backlash, and stick-slip make the use of controller outputs directly as model inputs ineffective and misleading. While reactant and fuel flows are typically measured, utility and reagent flows are often not. This short sightedness by plant projects (figuratively and literally), severely limits the ability to make improvements in the efficiency of use of these process inputs. I would wager a 10% reduction in the use of these inputs would more than pay for the flow meters. The old saying, you cannot control what you don't measure holds true for process efficiency. If I was a project manager, I would have a flowmeter on any input flow whose usage cost per year exceeded twice the installed cost of the flowmeter. I would at least provide the process connections for inserting a mobile wireless flowmeter. Where energy heat transfer rate calculations (e.g. heat removal rate as an inference of reaction rate) would be useful, I would install wireless RTD temperature transmitters on the streams entering and exiting the coils, exchangers, and jackets. Wireless transmitters allow the user to find during actual process operation the applications with the maximum benefit.