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Nov 11, 2011

Operational variance management can improve plant efficiency

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ABB South Africa|Africa|Design|Flow|IME SA|Industrial|Industrial Management Enhancement South Africa|Instrumentation|Measurement|PROJECT|Sustainable|System|Systems|Africa|Automation|Beverage|Chemical Analysis|Energy|Energy Efficiency|Energy Requirements|Equipment|Flow|Food|Maintenance|Real-time Measurements|Systems|Infrastructure|Kobus Van Der Merwe|Power|Measurement |Process Control
Africa|Design|Flow|Industrial|Instrumentation|Measurement|PROJECT|Sustainable|System|Systems|Africa|Energy|Equipment|Flow|Maintenance|Systems|Infrastructure|Power|Process Control
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Managing the variability of process parameters in subsystems using real-time measurements will ensure a higher chance of ideal running conditions and will improve plant efficiency and energy efficiency, said process control instrumentation and electrical equipment company Industrial Management Enhancement South Africa’s (IME SA’s) Dr Kobus van der Merwe.

Speaking at the Predictive Maintenance South Africa Conference in October, he said: “For every poor decision made by an operator, you decrease the life span of your equipment because it introduces instability into the process and places stress on the entire system of your production cycle.”

Inconsistent decision-making would mean that the inefficiencies already present in the process would be carried over, even after new technologies, systems and equipment have been implemented. However, how could operators or managers make correct decisions if they used lagging indicators to inform them?

“The challenge is that performance indicators only follow decisions made. Even if we only have a two-hour lag in terms of chemical analysis on production, it means that by the time we get the analysis, the process is long out of the door. If we want to apply any corrections that are needed based on the chemical analysis, the same process conditions no longer apply. Therefore, we take performance measurements in real time,” he explained.

IME SA and power and automation group ABB South Africa conducted real-time analyses of a number of pulp and paper, smelter, hydrometallurgical and food and beverage plants to determine their real-time efficiency and how this affected energy efficiency and process efficiency.

“We conducted our analyses using real-time measurements and identified different operational conditions in the production process.

“However, if we look at the time when a plant or subunit is operating in an ideal state, we see that plants only operate in the ideal state about 20% of the time. So, there is an 80% chance of improving the production process without buying new equipment or installing new systems,” said Van der Merwe.

“We take real-time measurements and define a link to performance indicators. If we can define a relationship, it provides us with the ability to say that, given the current operating conditions, which can be quantified in terms of tempera- ture and flow, besides others, we can say that the probability of hitting the target state is either higher or lower.”

If a plant could start to characterise subsystems and their performance parameters and only manage ideal conditions on each of the subsystems, then a plant would naturally achieve of higher throughput, he explained.

“Nonideal operating conditions were asso- ciated with energy requirements as high as 3 MWh/t (for one of the plants), which is about double what we are aiming for under ideal condi- tions. We can now start to understand what the plant conditions must be when we aim to reduce power consumption per ton.”

“Further, I can mention that increasing ideal time, from 30% of the time within its target limits at one plant in the study to 50%, led to a R20-million cut in its energy bill,” he emphasised.

The challenge was that overall equipment effectiveness (OEE) and other performance indicators were aggregated values, he noted.

“We sat with plant personnel and determined from them what they experienced as different operating conditions without taking actual real- time processing data into account. We then calculated that good and bad conditions produced very similar OEE values,” explained Van der Merwe.

Training and development of operational personnel and technical personnel, and aligning them in terms of their understanding of condi- tions and situations driving performance in a plant, contributed significantly to improving performance and increasing the chances of running in ideal conditions, he explained.

“A general comment from factory floors these days is that the experience is not there anymore but, throughout this whole process, what we noticed is that purely by highlighting and visualising those different process conditions, extracting the parameters from historical process data and design specifications provided a valuable understanding of the different process conditions operators need to monitor. The ability to identify and monitor a particular process condition is critical,” said Van der Merwe.

Meanwhile, asset sweating has emerged as a significant factor in efficiency.

“We completed an assessment and real-time measurement project at a plant that had planned to install a new induction furnace. This is on hold at the moment because the capacity made available through proper decision-making and operation was enough to make the new furnace redundant,” he emphasised.

Further, another important item was sustain- ability. Training staff to high levels, so that they understand a plant’s processes, was a more sustainable approach than implementing new technologies, he said, highlighting results from the study.

“The focus is primarily on training and development. No new systems were implemented or technologies installed and it is all about using existing infrastructure more effectively and effi- ciently,” noted Van der Merwe.

Recalibration of the measuring instruments should be done whenever a new piece of machi- nery is installed or when the characteristics of a piece of equipment change, which would affect the rest of the process.

Edited by: Martin Zhuwakinyu
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