Is this the time for process and automation system designs to minimize dead time and interactions? Is this the time for university graduates to understand controller modes and parameters and the power of the DCS environment? Is it possible for these graduates to know how to tune a controller with non perfect mixing, measurements, and valves? Is this the time for users to know how to improve control system performance? Is this the time for new engineers to have a virtual mentor? Is this the time for suppliers to be able to demo the value of better measurements, valves, tuning, and advanced control?

This could be the time if modeling is embedded in the DCS and becomes a common tool in universities and industry. It started to happen about 5 years ago. Terry Tolliver and Robert Heider at Washington University use an industrial DCS as a virtual plant and in a computer control lab to teach process modeling and control as part of their chemical and system engineering programs. Atanas Serbezov at Rose-Hulman Institute of Technology uses a DCS in a lab to teach process control to Chemical Engineers. A DCS system has just been installed at Purdue University as part of the Engineering Research Center with Rutgers and the New Jersey Institute of Technology. If you doubt the value, talk to the students. In industry, Broadley-James, Lilly, Lubrizol, Monsanto, and Solutia have started using embedded modeling in a DCS for process design and control.

Modeling was such an integral part of my career, it is difficult for me to imagine how I would have learned and accomplished anywhere near as much without it. Modeling was a key part of my job even in the old days when you had to key punch cards for the IBM Continuous Simulation Modeling Program (CSMP) and submit them for an overnight run in a room full of main frame computers. When I got terminal server access to a computer with the Advanced Continuous Simulation Language Program (ACSL), I thought I was in heaven even though ACSL was designed for the aerospace industry. When graphical flow sheet simulators on a PC came along that we could interface to a DCS, I was blown away even though the interface was slow and cumbersome and the model speedup was rather minimal and inconsistent. I gave this all up to retire in 2002 and set up the virtual plant at Washington University. This wasn't quite enough so I ended up in Austin in the fall of 2004 to see if I could help the future of modeling by making it more accessible. While only 50% of my 75% part time venture is actually doing modeling, I am at a juncture to see how well it can be used.

Eventually the automation world will evolve to where modeling is an integral tool for learning and 4D processes (development, design, deployment, and diagnostics). While my physics and process modeling background leads me to focus on models based on first principles, data driven models such as neural networks, linear dynamic estimators (e.g. MPC models), and multivariate statistical process control such as projection to latent structures (PLS) have great relevance because they have make no assumptions and detect the inevitable unknowns. I envision a future of hybrid models embedded in the DCS that use the best that each of these modeling technologies offers. Is this the time?