PERFORMANCE
MONITORING
Condition monitoring
for membrane systems
This paper outlines a new solution developed by ABB for advanced operation
of membrane processes, specifically for reverse osmosis and nanofiltration
W
ater stress is of increasing relevance on a global
scale. More and more regions are affected by it. As
of up to now, it is estimated that 700 million people in
43 countries are already facing water supply issues. Amongst
others, water stress provides for negative impacts on health
(reoccurrence of specific diseases for example), security
(for example, risk of war on access to water sources) and
economic issues (prohibition of growth due to lacking water
suppliers). Available projections show a dramatic increase of
countries affected by water stress in the next 10 to 15 years.
Impacts of water stress will get more and more observable,
for example, in the South Western part of the United States,
the Mediterranean region, the Arabian Peninsula up to China
or in Australia (see Figure 1).
Two thirds of the world’s population are expected to live in
water-stressed countries by 2025 – from originally expected
one third of the global population. The revision of this estimate
demonstrates that it is important to keep a stronger focus on
the topic of water stress, taking also into consideration that
the total world’s water consumption is expected to further
increase by 40% in 2025.
Desalination
To overcome water stress and the
related impacts, water desalination
is one of the measures to support
future water supplies security.
Desalination is used for desalinating
brackish water and seawater. Due
to improvements in design and
materials, capital and operational
expenditures have been optimised
over recent years – desalination
is now a mature technology being
increasingly attractive from an
economic point of view as a
water treatment technology. For
desalination, different process
technologies can be applied:
• Thermal desalination
technologies such as multistage
flash (MSF), multi-effect distillation
(MED), or vapour compression (VC) using the effect of
evaporation/distillation.
• Membrane desalination technology such as reverse
osmosis (RO) or nanofiltration using the effect of physical
separation based upon pressurising feed water and passing
it through a semi-permeable membrane.
The most dominant process technology in recent years in
terms of number of installations and additionally contracted
capacity is reverse osmosis and this trend is expected
to be continued as reverse osmosis provides for a much
higher flexibility in terms of operation. Thermal desalination
technology is strongly used throughout the Arabian Peninsula
and is expected to play an important role in this region in the
future.
Energy consumption
A critical factor in desalination plant operations is energy
consumption. While for a 20-year life cycle cost calculation,
energy consumption is about 50% for thermal desalination
(mainly steam energy is required), energy consumption
represents 30% to 50% of life cycle costs for membrane
processes with electrical energy mainly required for
Figure 1: Water Stress Map 2025
Water scarcity
Increasing
level of water
scarcity
Sufficient
water
resources
Water surplus
Water & Wastewater Asia • December / January 2011
35
PERFORMANCE
MONITORING
Membrane performance
pressurising feed water. In the following
Figure 2: Reverse osmosis cost breakdown.
estimate for reverse osmosis (Figure 2),
electrical energy makes up 44% of the
total life cycle costs.
7%
5%
4%
Capital costs
Comparing different membrane
37%
3%
Electrical energy
systems, the energy use in reverse
Chemicals
osmosis and nanofiltration is higher
as higher levels of pressure need to
Membrane
be applied following required process
Labour and capacity charges
conditions. In the case of reverse
Maintenance
44%
osmosis, the pressure levels are required
to overcome the osmotic pressure of the
solute as well as to cause transport of
the solvent from feed side to permeate
side (product water).
“concentration polarization”. This accumulation causes fouling
Inefficiencies in plant operations cause higher energy
and thus blockage of membranes which ends up in reduced
consumption and thus negatively contribute to climate change
productivity (lower permeate flux). The decreasing membrane
which impacts the water supply situation as climate change
performance goes along with an increased differential pressure
is a main cause for water stress. Therefore, it is of utmost
between feed water and reject water side and a decreasing
importance to further improve energy efficiency in treatment
permeate flux (see Figure 3).
processes in general and desalination in specific.
The phenomenon of fouling is outlined in Figure 4 on
page 37:
Better management of operations
As fouling is a highly dynamic, non-linear process (see
For desalination, the need for better management of operations
Figure 5) – depending on operational (feed pressure, feed
moves on the agenda in order to reduce energy consumption,
flow) as well as environmental conditions (for example, feed
to minimise costs and to optimise productivity.
water temperature or salt concentration) – an online analysis
Required are energy-efficient, highly reliable products
of the root cause is more or less impossible.
and solutions, the consideration of the life cycle cost related
aspects right from the conceptual stage as well as solutions
Taking proper maintenance actions
for process optimisations integrated into the operational
In order to take proper maintenance actions, it is required
environment to provide for highest benefits.
to be aware of the condition of the membranes, taking into
This paper outlines a new solution developed by ABB for
consideration the fouling and its dynamic nature. Increased
advanced operation of membrane processes, specifically for
fouling leads to energy-inefficient operation as the specific
reverse osmosis and nanofiltration.
energy required to produce 1 m³ of permeate increases.
Membrane systems in general are applied in different
Typical maintenance measures to overcome fouling are:
treatment applications including the desalination of brackish
• Backwashing
and seawater, the water and wastewater treatment as well as
• Chemical cleaning
water reuse. The objective is to remove unwanted particles and
• Partial membrane replacement
to produce a product water stream fulfilling quantitative and
• Total membrane replacement
qualitative requirements. Different technologies are applied,
In addition to the above given options, also operational
starting with media filtration for removal of macro particles
ending with reverse osmosis to remove particles belonging
Figure 3: Impact of decreasing membrane
to molecular range size. Unwanted particles cover include
performance.
organic (for example, bacteria) or inorganic (for example,
salts) contaminants.
Differential
While for water and wastewater treatment, pressure
pressure
levels are moderate ranging roughly from 1 to 5 bar (for
example, media filtration application), pressure levels for
desalination range from 5 to 80 bars causing high levels of
energy consumption. The challenging aspect is now to get an
indication of the optimal operation set-points to achieve most
energy-efficient and productive operation as these change
Permeate
flux
over time with a changing operational characteristic of the
membranes.
As the solute passes through the semi-permeable
membrane, particles accumulate on the membrane surface
Time
of the pressurised feed water side leading to so-called
36
Water & Wastewater Asia • December / January 2011
PERFORMANCE
MONITORING
Figure 4: Particles accumulation on membrane
surface.
Support
layer
Product
water
Fouling Thickness
Fouling Rate
Fouling rate and Fouling
thickness
Reject
Separation
layer
Figure 5: Dynamic nature of fouling.
High pressure
Inorganic and
organic particles
Feed water
Semi-permeable
membrane
Time
Results in
membrane fouling
Tota
repl l
acem
ent
ent
Par
repl tial
acem
ent
Par
repl tial
acem
Performance Indicator
2 nd
1 st F
lush
ing
Flus
h
ing
3 rd
Flus
hing
1 st C
hem
clea ical
ning
Part
i
a
l
repl
acem
ent
conditions (set-points) can be optimised to minimise fouling Performance monitoring
and to achieve higher productivity. This needs to be addressed The function of the performance monitoring module is to
in a way considering the actual level of fouling (membrane calculate selected key performance indicators (KPI) that reflect
condition) and to run a prediction based upon a model-based the dynamic nature of fouling and provide for proper monitoring
approach considering real-time and historical data.
of the membrane fouling condition. The calculation of the
Different approaches are available to determine the optimal KPIs is done using a first principle model as basis. For the
point in time to take a maintenance measure and various R&D calculation, nominal process data, such as feed temperature,
initiatives are going on addressing this specific operational feed pressure, differential pressure or reject flow rate, are
aspect. A typical approach is to follow the recommendation required – no additional measurements are required and thus
as given by the membrane supplier, typically following an the solution can be added to new or existing installations,
indication of predetermined time periods. To demonstrate the
The calculation is done on a train-by-train basis and
drawbacks of this approach, the following points should be the trains are described by models. As it can be seen from
taken into consideration:
• If condition of membranes does not
Figure 6: Key performance parameters reflecting dynamic
require maintenance, additional costs (for
nature of fouling
example, for chemicals) and production losses
might be the result.
• If membranes are in a condition that
cleaning is overdue, membranes might
have already been damaged up to a point
where even chemical cleaning does not give
required improvements in terms of restoring
productivity.
FP 1
Even approaches that are based on
Monitoring with
cleaning and
differential pressure of feed on reject side do
flushing
not provide for capturing the dynamic nature
of fouling.
Thus, a new approach has been developed
to overcome drawbacks of existing approaches
Monitoring
FP 2
without
such as the aforementioned ones and to provide
cleaning and
for online, real-time conditional assessment.
flushing
The developed solution consists of two
modules, one to cover the functional scope of
Initial state 1st Cleaning 2nd Cleaning 3rd Cleaning 4th Cleaning
performance monitoring, the second to cover
Time
operation optimisation.
Water & Wastewater Asia • December / January 2011
37
PERFORMANCE
MONITORING
Figure 7: Examples for alarm limit configuration.
– membrane requires cleaning, for example, < 15 days
Red
Yellow
– membrane requires cleaning, for example, < 15 days > 60 days
Green
– membrane requires cleaning, for example, < 60 days
Figure 8: Results visualisation giving membrane condition and estimated due date per train.
Last Calculation
10:30am, Monday, 14 Sept 2009
Unit 1
Trains
Train 1
Train 2
Train 3
Train 4
Train 5
Unit 2
Actual Feed
Pressure
67.1 bar
66.4 bar
66.1 bar
65.2 bar
65.9 bar
Unit 4
Unit 3
Actual Feed
point
67.2bar
66.3bar
66.0bar
Optimal Feed
Pressure
67.2bar
66.3bar
66.0bar
65.3bar
65.8bar
65.3bar
65.8bar
Unit 5
Actual Feed
Flow
680m3/hr
671m3/hr
669m3/hr
690m3/hr
685m3/hr
Current Time:
9:30PM, Monday, 14 Sept 2009
Unit 7
Unit 8
Optimal Feed
Actual set Point flow
682m3/h
682m3/h
672m3/h
672m3/h
Due Date For
cleaning
26 Sep 2009
22 Oct 2009
Unit 6
667m3/h
691m3/h
684m3/h
667m3/h
691m3/h
684m3/h
21 Nov 2009
26 Jan 2010
10 Mar 2010
Figure 6, the two main KPIs show an adverse effect with occurring fouling: While the one KPI increases (FP2), the other one
decreases (FP1).
A combined analysis of both allows getting an insight to the actual condition. The calculation is done over time on a regular
basis, for example, every three hours. Once required, for example, in case membranes have been chemically cleaned, the
model is tuned to reflect the real plant behaviour and characteristics.
A prediction based on actual as well
as historical data using the tuned model
Figure 9: System architecture.
provides for an estimated due date for taking
chemical cleaning measures (advisory
OPTIMAX® Membrane
function). Based upon pre-configured limits,
Performance (Client)
the status of the train is indicated using
Updated
colour coding, for example, if cleaning due
Online Membrane
Online Membrane Parameter
Process
Performance
date is estimated to be within the next 15
Optimisation
Monitoring
days period, the train condition is colourcoded in red on the operator screen (see
example as shown in Figure 7). The alarm
Optimal operating
Fouling status
limits can be flexibly defined.
condition
PGIM
Thus, an intuitive and easy way of
Sever
working and applying the solution is
provided. The performance monitoring
is applicable for different membrane
configurations such as hollow fibre and
DCS Interfaces
DCS Interfaces
it does not require additional sensors
required. It considers the hydrodynamics
of membrane fouling at the membrane
surface and it addresses the complete
membrane life cycle except for partial
replacement.
The performance monitoring function
RO unit 2
RO unit 1
also allows an assessment of the quality
3rd party DCS (ABB or 3rd party)
ABB DCS
of taken maintenance measures by
DCS
comparing the condition before and after
38
Water & Wastewater Asia • December / January 2011
PERFORMANCE
MONITORING
Figure 10 – Exemplary Operator Screen used to present membrane
performance monitoring and optimisation
RO Unit 1
Train F
Train H
RO Unit 4
RO Unit 3
RO Unit 2
Train H: RO Monitoring and Optimisation Solution
Permeability
Close
Lest Calculation Time
10.12.2009 05:99:00.000
Current Time
10.12.2009 11:26.54
268.16
0.00
299.11
Optimal Values
Actual Set Point Optimal
Product flow rate (m3/rr)
and can – if required – also be transferred
to the process control system using
standard interfaces for visualisation,
for example, in alarm lists. Extensive
reporting function is available with the
information management system. Reports
can be created in Microsoft Office and
can be deployed as html files on the web
server of the information management
system – the reports are accessible and
viewable using thin client technology.
Successful implementation
the maintenance measure using an analysis of the main two
key performance indicators.
Operation optimisation
The second module addressing operation optimisation uses
results from performance monitoring (performance prediction)
as the basis. Optimal operation conditions are calculated
considering the operational and physical constrains. Depending
on whether variable frequency drives (VFD) are used to drive
the pump motors or not, either feed pressure and feed flow
(VFDs used) or feed pressure or feed flow (operation without
VSDs) set-points can be calculated. As aforementioned, the
fouling rate dynamics depend on the operational set-points
(feed flow, feed pressure) – this is considered for the calculation
of the optimal set-points as the calculation not only aims at
increasing productivity levels but also to optimise the fouling
rate. The optimisation can be run regularly and might be
implemented for open open-loop or closed-loop operation.
The optimal set-points are suggested by the system follow
the operational and physical constraints.
In order to have high flexibility in terms of application,
the modules are based on an ABB information management
system which is used for data handling, storage and information
management. This is outlined in Figure 9.
The information management system is capable of
consolidating data from various process control systems being
the source of required process data. In addition, the system
provides for visualisation of results, for example, using trends
of lists and even more extensive features such as alarm
management (see Figure 10).
Results from the performance monitoring and optimisation
solution are stored in the information management system
The implementation of the performance
monitoring and optimisation solution has
successfully been implemented using
afore-described architecture. With the
pilot it was possible to demonstrate that
the solution is capable of capturing the
dynamics of fouling in real-time and to
well give an insight to the membrane
condition. Applying the optimisation
function, it is possible to reduce the gap
between actual and optimal set-points by
gradually applying optimal set-points and
thereby to increase productivity. By gradually implementing
optimal set-points, it was possible to achieve a 2% productivity
increase during the pilot and to optimise the fouling rate.
Optimal set-points have not been fully applied, so additional
improvement potential by further implementing the suggested
optimal set-points.
Benefits
In terms of benefits, the solution maximises the productivity by
allowing to get higher product flow rates. In addition, operation
and maintenance costs are minimised by improving the energy
efficiency and lowering the amount of chemicals required for
cleaning as cleaning measures follow the condition of the
membrane system. The membrane lifetime is increased as the
risk of membrane damage is minimised following conditionbased membrane maintenance measures. Unbudgeted
membrane replacement can be avoided. Besides all this,
the plant availability is increased by lowering cleaning and
replacement activities and thus reducing plant downtimes.
The solution is applicable to different membrane configurations
such as hollow fibre and can be used with existing or new
installations without requiring additional measurements.
Overall, with this newly developed approach, the
maintenance process for membrane systems can be changed
from reactive, preventive to a predictive, condition-based way
of operation. WWA
This paper is written by Mr Markus Gauder (business unit
power generation, ABB AG, Mannheim, Germany), Mr
Senthilmurugan S (ABB Corporate Research, Bangalore,
India) and Mr Marc Antoine, (ABB Switzerland, business unit
power generation, Baden, Switzerland).
Water & Wastewater Asia • December / January 2011
39