Six Sigma Alarm Management

By Brent J. Thomas

Monsanto’s plant in Soda Springs, Idaho, produces elemental phosphorus, or P4.  Most of the P4 is sent to other Monsanto facilities in Luling, La., and Camacari, Brazil, to make PCL3, the primary ingredient for Roundup herbicide. The remainder ends up in a variety of products including foods, cleaners, water treatment, flat-panel televisions, oral care products, paints and coatings, and pharmaceuticals. Specific applications of Soda Springs elemental phosphorus include the manufacture of Skydrol hydraulic fluids for aircraft, Phos-Chek fire retardant for fighting forest fires and Dequest water purifier.

The Process

The process of manufacturing elemental phosphorus begins with mining the phosphate ore from the Rasmussen Valley mine thirty miles north of Soda Springs. The ore is trucked to the site on Monsanto’s private highway and stockpiled.

The ore is fed to a rotary kiln, much like a cement kiln, 325 ft long and 16 ft in diameter, rotating at about 3.5 rpm.  Organic material is burned out of the ore, and nodules are formed.  The nodules are then mixed in batches with quartzite and coke and fed to one of three electric arc furnaces.  The three furnaces combined consume approximately 150 megawatts of electricity. The phosphorus leaves the furnaces as an off-gas and is condensed for shipment.

The Situation in Soda Springs

The furnace process at Soda Springs is a dynamic one that requires sophisticated controls. Monsanto began using Fisher’s PRoVOX distributed control system (DCS) in the early 1980s. By about 1996, Fisher had become Emerson, and development of the DeltaV control system had begun. 

Figure 1. A PB4Y fire bomber drops Phos-Check on a brush fire. The bright red retardant fades in the sun and eventually decays into fertilizer.

Soda Springs is nearing the end of an eight--year project to convert its PRoVOX DCS to DeltaV. Both control systems provide sophisticated alarming capabilities which in many ways are far too easy to implement. Back when an alarm meant installing a sensor and cable, which cost significant dollars, we carefully evaluated the alarms according to need, and the number was kept to a minimum. With the advent of digital control systems alarms became easy and cheap (almost free). The result was gross overuse of the alarming capability, which caused as many problems as it solved.

Monsanto began putting an emphasis on Six Sigma practices in 2001 and an emphasis on alarm management in 2002. Both corporate and local teams were formed to assess the state of the alarm systems within Monsanto and define a path forward to develop an effective, efficient alarm methodology and system. The local team decided to apply the new methodology to one furnace first to get a feel for what we were up against. This furnace was due for conversion to DeltaV and presented some opportunities for improvement.

Where Do We Go From Here?

How to analyze the present state and how to improve the situation were the biggest questions. The answer to both was the DMAIC (Define, Measure, Analyze, Improve, Control) methodology model of the Six Sigma process. After two years of work. Monsanto arrived at a firm alarm philosophy and had corporate-wide standards for analysis, improvement and key performance indicators (KPIs).

The first step in the analysis was to ask, just what is an alarm system and what should it look like?

Figure2. Typical control room alarm boundaries and appropriate d operator responses.

The purpose of an alarm system is to alert the operator of conditions that require attention. The ideal, of course, is to stay in the green target operation area, but this would require an unrealistically stable process—one that wouldn’t need a DCS.  The next best thing is to stay within the yellow area, which leaves the operator free to optimize operations. This also requires a mostly stable process that can be completely controlled by the DCS alone. Unfortunately, our furnace processes are extremely dynamic, and operator intervention is a fact of life.  However, we can minimize operator workload by controlling the number of total alarms by eliminating those that are not necessary.

Problem Statement(s)

Our alarm problems came down to these:

1. Too many alarms. Alarms that don’t mean anything desensitize operators to the ones that do.
2. Too many alarms at once. Alarm floods allow for no reaction time.
3. Too many alarms had no defined response. Operators sometimes had no way to react to an alarm.
4. Poor alarming practices can cause incidents. (See Three Mile Island and Chernobyl).

We used the Six Sigma cause-and-effect fishbone diagram to investigate the possible causes of our alarm problems. We found that while we did a good job of selecting and maintaining instrumentation, our methods were the root cause of the alarm system state—we had no philosophy or guidelines for configuring alarms.

Solution

At this point, we applied the Six Sigma DMAIC system to the problem. First we developed an alarm management philosophy and selected alarm metrics (the Define phase). Then we assessed the present alarm system (Measure/Analyze phases). We reduced the number of nuisance alarms (Analyze /Improve phases). We rationalized alarms by need and priority (Improve phase). Then we developed an alarm configuration database (Improve – Control phases). Finally, we will implement knowledge-based alarming where appropriate.