Is your radial turbomachinery operating at optimum?

Bryan Research and Engineering, Inc. (BRE) has developed an online, equipment performance monitoring application for radial turbo-machinery in cryogenic gas plants.

Charles C Solvason and Barry Burr
Bryan Research & Engineering, Inc.

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Article Summary

The application works by capturing process measurement data in real-time and placing it in the ProMax® simulator for analysis. The modelling capabilities of ProMax are then leveraged against a new, empirical representation of momentum transport to deliver actual versus target performance indicators, such as efficiency, head, and power at off-design conditions. The results of the performance indicators are then returned to a data historian and a customisable display is used to monitor the performance of the turbo-machinery from anywhere on the network. As a result, engineers and operators now have the ability to make informed process decisions based on equipment performance adjusted for off design conditions. This paper highlights the application and its benefits using a case study with real-time data from a cryogenic gas train.

1. Introduction
Bryan Research & Engineering, Inc. (BRE) is a widely recognised provider of software and engineering solutions to the gas processing, refining and chemical industries. Since the company’s inception in 1974, BRE has combined research and development in process simulation, equipment rating, and state-of-the-art computerised engineering technology to provide our satisfied clients with flexible, accurate, efficient, and dependable tools that ultimately improve their bottom line. Built on the foundation of its renowned predecessors, TSWEET® and PROSIM®, BR&E’s ProMax® process simulation package provides design solutions using equipment ratings, state-of-the-art thermodynamic property packages, a large chemical species database, oil characterisations, solvers, and OLE automation tie-ins.

Utilising the design capabilities of ProMax in a service role for online performance monitoring and optimisation represents a significant opportunity for gas processors. Benefits include determining the properties, compositions, and phase behaviour of all streams in the process including solid formation temperatures and cricondentherm and cricondenbar limits in real-time. Additionally, performance measures for equipment can be monitored, including column capacity and flooding, heat exchanger fouling, equilibrium approaches to absorbers in amine plants, and radial turbomachinery performance metrics.

The performance of radial turbomachinery is typically described by equipment performance curves or, in some cases, limited empirical models provided by the equipment manufacturer. The curves can be used to determine the optimum flow rate or shaft speeds needed to maximise the efficiency or power of the equipment. However, as the inlet gas conditions move off the design point, the design curves no longer represent the equipment’s performance. This limitation can hinder prospective optimisation schemes, resulting in significant plant-model mismatch and a diminished capability of gas processors to maximise the performance of their assets in real-time.

In addition, mechanical deterioration that alters the machine’s geometry, such as fouling or vane deformation, can also reduce the equipment’s performance at off-design conditions. Fouling is typically addressed with a series of scheduled maintenance shutdowns that can quickly become expensive. Continuous monitoring of turbomachinery performance allows engineers to detect events which cause deteriorating performance. This could lead to guidelines for avoiding certain situations. Also, maintenance shutdowns could be scheduled on performance criteria rather than with a fixed schedule.

To help our customers address these performance monitoring limitations, Bryan Research & Engineering, Inc. (BRE) has developed (1) a real-time performance monitoring application that communicates with our ProMax® simulator and OSIsoft’s Plant Information (PI) system and (2) a set of radial turbomachinery performance metrics to describe off-design conditions. Communication with other process data historians will be developed on an as-needed basis. This paper reviews the monitoring application, the performance metrics, and highlights a case study using real-time gas plant data.

2. Monitoring Application

BRE created a PI application that communicates with OSIsoft’s Plant Information (PI) system for the purpose of utilising ProMax in a service role. As shown in Fig. 1, the application: (1) pulls PI-tag information from an XML configuration file, (2) uses that information to call the PI-Server and take snapshots of PI-data, (3) loads the information into a ProMax project and solves it, (4) if selected, performs analysis calculations, and (5) returns the results to the PI system where they are (6) accessed by anyone connected to the PI-Server network. The XML file contains each analysis’s tag names and equipment specifications inherent to the equipment being modelled in ProMax. All of the equipment specific to a single plant train are located in a single project. The analysis tool is capable of being instantiated multiple times on the client PC and works exclusively with ProMax.

3. Performance Metrics
ProMax currently calculates turbo-machinery metrics using thermodynamic models and mass and energy balances. The metrics include the actual adiabatic efficiency and power for a radial turbine, the actual polytropic head, polytropic efficiency, and power for a centrifugal compressor, and all of the above plus bearing losses for turboexpanders. To evaluate the off-design performance of the equipment, BRE developed new, empirical representations of angular momentum transport using the Buckingham PI-theorem (BPT). An empirical representation of the angular momentum balance (aMoB) was chosen for this application in order to ensure algorithm convergence when operating in an online capacity. Parameters for the new empirical models are determined by fitting reconfigured turbo-machinery design expressions against a combined momentum, mass, and energy balance at off-design conditions.

3.1. Radial Turbine Models
According to Whitfield and Baines [1], the basic parameters that influence the performance of a turbine are wheel tip diameter, shaft speed, mass flow rate, molecular weight, heat capacity ratio, viscosity, and the pressures and temperatures of the streams entering and exiting the turbo-machinery. These 10 terms can be reduced to 6 non-dimensional variables using the Buckingham π theorem. For a fixed diameter radial expander typically used in cryogenic NGL recovery processes, these variables can be further reduced to pressure ratio (PR), adiabatic efficiency (η), flow coefficient (Q/N), speed ratio (U/C0), and constrained heat capacity ratio (γc).


The flow coefficient and speed ratio help to describe the flow regime of the fluid in the expander, while the pressure ratio helps to describe the mechanical geometry. Each of the terms can be parameterised separately. The flow coefficient and speed ratio can be parameterised using the design curves provided by the manufacturer. The pressure ratio, however, requires additional data at off-design conditions which must first be created using the extra degree of freedom gained by utilising the momentum balance. In most cases, the momentum balance reduces to Euler’s turbo-machinery balance with the assumption of negligible or slightly negative axial rotation (≥ -30°) entering the rotor and negligible radial rotation exiting the rotor [1]. In some cases, especially near the boundaries of the operating envelope, these assumptions require revision, either by adding geometric complexity, or by correcting the expression using loss mechanism empirical models. Common loss mechanisms include leakage, friction, passage, and incidence losses, among others [2].

Finally, the constrained heat capacity ratio can be utilised to describe compositional changes in the gas stream. Unlike most applications, however, cryogenic gas streams typically phase separate in expanders, significantly changing the composition of the vapour phase, and altering its performance. This dynamic behaviour is limited by constraining the heat capacity ratio and off-design performance to an operating region exhibiting similar phase separation characteristics.

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