Take equipment diagnostics to a new level

Many of the effects of machine degradation are avoidable, so being able to identify struggling equipment at an early stage is essential to renewing its productivity. Advances in performance monitoring can provide critical insights.

By Todd Anderson

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As aging process and mechanical equipment begins to deteriorate, its energy effectiveness decreases, power consumption rises, efficiency drops and operating costs escalate. Many of the effects of degradation are avoidable, so being able to identify struggling equipment at an early stage is essential to renewing its productivity — adding dollars to your bottom line. That’s why it’s so important to be aware of the performance of crucial equipment, including key compressors, heat exchangers, steam boilers, turbines and pumps.

Most chemical makers accept an overall loss of efficiency as largely inevitable, part of running a large complicated process. They try to maintain output by pushing the machinery harder and using preventive maintenance and periodic shutdowns. However, preventive maintenance isn’t the answer to avoiding serious problems with your most critical machinery.

Truth is this drop in efficiency stems from small losses throughout the process that can be corrected only when properly identified. The big utility companies, for example, are constantly making strategic efficiency calculations to know where losses are occurring so that a maximum amount of power can be generated for the cost. A similar opportunity now exists for chemical companies to obtain information on current efficiencies of critical equipment that may not be easy to assess — and to achieve true predictive maintenance, which is considerably more cost-effective than schedule-based preventive maintenance.

Advances in equipment performance-monitoring technology coupled with third-party expertise can provide specific details about performance losses of essential machinery along with recommendations for raising the performance of that equipment to acceptable levels. The results can be impressive, for example:

  • Detection of a significant efficiency loss of a single critical compressor enabled one company to take corrective action that resulted in a financial benefit of $3 million per year.
  • Avoiding an unplanned shutdown saved another company $2.6 million.
  • Modifying operation within a heat exchanger network reduced power consumption by 2% without a change in outlet temperatures.

Achieving such results doesn’t require managers to look through a lot of data.

Instead, specialists at the third-party keep them informed and advise about alternatives for any evolving situation that could impact productivity. Performance monitoring gives the managers the hard facts and financial justification to make decisions that affect the entire plant and allow equipment to be optimized for extended run times and maximum productivity. This leads to improved maintenance cycles with lower costs, fault detection and rectification, process improvements identified through performance behavior trends, and greater productivity. Performance deficiencies are minimized, energy costs are reduced, production is maximized, and payback is assured.

Leveraging existing data

Plant equipment produces lots of raw data that reflect its performance — data that could reveal efficiency losses. The problem is that most plant personnel don’t have the time or expertise to do the necessary engineering calculations to determine whether a pump is delivering the intended fluid volume at full throttle or if a boiler is generating a certain amount of steam and, if not, why not. Staff members have other legitimate concerns but eventually the declining performance of some piece of critical equipment will grab their attention. By that time, thousands — even millions — of dollars may have been wasted.

Minimizing such performance degradation and its resultant costs requires a structured performance-monitoring approach based on thermodynamic models that go beyond the calculations many plants already use to simply generate performance data.

Thermodynamic-based performance models cover the expected operating envelope of the equipment of interest as well as overlaying predictions onto the actual plant data to provide a means of confirming the model’s fidelity. In this way, you can continuously and accurately check the condition of your most important assets.

Compressors, boilers and steam or gas turbines are most commonly addressed but a thermodynamic model can be computed on any piece of equipment subject to changes in temperature, pressure and flow. Then, data commonly produced to meet a plant’s environmental, health and safety requirements are compared to the customized model for a picture of how well that machine is actually performing — to determine efficiency loss versus like-new or best case operation. While end-users may be aware that the performance of a piece of equipment is below normal, the extent of the loss can be a shock, and they rarely know what’s causing the degradation.

The most important element of this performance monitoring package is the expertise required to build the thermodynamic model and then distill and validate the large amount of production data from critical pieces of plant equipment. Most plants lack such capabilities and so rely on a third party that has performance specialists. By using the model to analyze this information and formulate actionable recommendations, these experts are able to obtain information about lagging performance that’s never before been available.

For example, using the thermodynamic model of a multi-stage compressor (Figure 1), specialists can determine the current performance of the overall compressor, on each stage of the compressor and, if there are inter-stage coolers, the performance of those coolers as well. In addition, they can define the negative impact of each one of those different sections of the compressor on the machine’s overall performance and show what it’s costing to drive the compressor or how the compressor’s throughput has been reduced (Figure 2).

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