In Parts 1 and 2 we discussed simulating your Reciprocating compressor performance from the computer based simulators available from most compressor manufacturers such as Ariel Corp., Dresser-Rand or G.E. or other programs of this nature. The data required for input, the simulation results and possible problems that may arise were touched on and now we have found ourselves needing to verify some results with field analysis.
Field analysis of reciprocating compressors takes some specialized equipment and techniques to ensure you are accurately reporting the machinery performance and health. There are only a couple of products available out there such as the Windrock 6320 or the Dynalco Recip-Trap 9260. After the equipment there is alot of training involved in utilizing the equipment properly and interpreting the results. Contractors are becoming more common place in recent times, however not all contractors are created equal. As with any service, do your research and select a contractor that has the experience and track record to be able to provide reliable data results.
So, now after we found a huge discrepancy with our simulation results from the unit flow meter and load from the engine/motor driver data we need to verify what we are finding. We will assume you have selected a good contractor to provide the compressor analysis service, so we will get to analysis and data capture. Reciprocating compressor performance testing can be a lengthy exercise, though not as detailed in instrumentation as testing centrifugal compression. The testing conditions are key to getting accurate data and providing a good basis to make optimizing improvements to the machinery.
There are three key areas to focus on as an analyst and the first and most important is the reference marker for the compressor. An error of 1° of crank rotation at this point, can drastically affect the calculations for unit flow and horsepower consumption. Using a simple procedure, verifying the true phasing of the compressor will avoid any misleading results and remove any doubt on the reliability of the data. Secondly the accuracy of data can be affected with the pressure sample or indicator valves installed in the compressor cylinders. A full port “roddable” needle valve is the one I like the best as it can be purchased in various metal compositions for all different gasses and also can be plugged off for safety concerns. These two areas are the most sensitive as far as the horsepower calculations. The third part is something we utilized in the simulation and that is the gas analysis. This data is used to calculate the unit flow and theoretical temperatures for comparison to the field temperatures measurements.
Since the reciprocating analyzer is actually measuring the internal Pressure-Time (PT) and in turn the Pressure-Volume (PV) data for the compressor cylinder, the gas analysis is not going to affect the horsepower calculation. This is hard to get a handle on if you have run simulations in the past as the gas analysis will affect how the simulator programs see the compressor behave and in turn change the horsepower. As far as the analyzer is concerned all it is measuring is pressure and phase angle to produce a calculation of horsepower based on the geometry of the unit. After that, the math from the gas analysis is included to produce flow estimates. These are important for the evaluation of the unit health for common problems such as ring or valve leakages.
Ok, we now have field analysis data and the contractor reports a flow very close to the field flow meter and confirms that our simulation is wrong. Now what? Well from the data in our field analysis we have determined that we have found a huge pressure drop between stages. That is significantly increasing discharge temperatures and now also reducing the unit flow compared to the simulation results. Also found was a discrepancy in the clearances reported in the data for the simulation and the actual clearances determined from the analyzer data.
In Part 4 we are going to go over the fine tuning of the simulation from the field analysis data for more accurate predictions and simulations of future running conditions.
Jason Hoffman, C.E.T. at EMS Inc.
