Condition monitoring for membrane systems

Transcription

1 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 Water 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 specifi c 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 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 improvems in design and materials, capital and operational expenditures have been optimised over rec years desalination is now a mature technology being increasingly attractive from an economic point of view as a water treatm technology. For desalination, differ process technologies can be applied: T h e r m a l d e s a l i n a t i o n technologies such as multistage fl ash (MSF), multi-effect distillation Water scarcity Increasing level of water scarcity Suffici water resources Water surplus (MED), or vapour compression (VC) using the effect of evaporation/distillation. Membrane desalination technology such as reverse osmosis (RO) or nanofi ltration 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 rec 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 fl exibility 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 repress 30% to 50% of life cycle costs for membrane processes with electrical energy mainly required for Figure 1: Water Stress Map 2025 Water & Wastewater Asia December / January

2 pressurising feed water. In the following estimate for reverse osmosis (Figure 2), electrical energy makes up 44% of the total life cycle costs. 5% 4% 7% Comparing differ membrane systems, the energy use in reverse 3% osmosis and nanofiltration is higher as higher levels of pressure need to be applied following required process conditions. In the case of reverse 44% osmosis, the pressure levels are required to overcome the osmotic pressure of the solute as well as to cause transport of the solv from feed side to permeate side (product water). Ineffi ciencies in plant operations cause higher energy consumption and thus negatively contribute to climate change which impacts the water supply situation as climate change is a main cause for water stress. Therefore, it is of utmost importance to further improve energy effi ciency in treatm processes in general and desalination in specifi c. Better managem of operations For desalination, the need for better managem of operations moves on the agenda in order to reduce energy consumption, to minimise costs and to optimise productivity. Required are energy-effi ci, highly reliable products and solutions, the consideration of the life cycle cost related aspects right from the conceptual stage as well as solutions for process optimisations integrated into the operational environm to provide for highest benefi ts. This paper outlines a new solution developed by ABB for advanced operation of membrane processes, specifi cally for reverse osmosis and nanofi ltration. Membrane systems in general are applied in differ treatm applications including the desalination of brackish and seawater, the water and wastewater treatm as well as water reuse. The objective is to remove unwanted particles and to produce a product water stream fulfi lling quantitative and qualitative requirems. Differ technologies are applied, starting with media fi ltration for removal of macro particles ending with reverse osmosis to remove particles belonging to molecular range size. Unwanted particles cover include organic (for example, bacteria) or inorganic (for example, salts) contaminants. While for water and wastewater treatm, pressure levels are moderate ranging roughly from 1 to 5 bar (for example, media fi ltration 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-effi ci and productive operation as these change 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 of the pressurised feed water side leading to so-called Figure 2: Reverse osmosis cost breakdown. concration polarization. This accumulation causes fouling and thus blockage of membranes which ends up in reduced productivity (lower permeate flux). The decreasing membrane performance goes along with an increased differial pressure between feed water and reject water side and a decreasing permeate fl ux (see Figure 3). The phenomenon of fouling is outlined in Figure 4 on page 37: As fouling is a highly dynamic, non-linear process (see Figure 5) depending on operational (feed pressure, feed fl ow) as well as environmal conditions (for example, feed water temperature or salt concration) an online analysis of the root cause is more or less impossible. Taking proper maintenance actions In order to take proper maintenance actions, it is required to be aware of the condition of the membranes, taking into consideration the fouling and its dynamic nature. Increased fouling leads to energy-ineffi ci operation as the specifi c energy required to produce 1 m³ of permeate increases. Typical maintenance measures to overcome fouling are: Backwashing Chemical cleaning Partial membrane repl Total membrane repl In addition to the above given options, also operational Membrane performance 37% Capital costs Electrical energy Chemicals Membrane Labour and capacity charges Maintenance Figure 3: Impact of decreasing membrane performance. Permeate flux Differial pressure 36 Water & Wastewater Asia December / January 2011

3 PERFORMANCE 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 Results in membrane fouling Tota repl l Par repl tial Par repl tial 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 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 Differ 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, differial pressure or reject flow rate, are aspect. A typical approach is to follow the recommendation required no additional measurems 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 improvems in terms of restoring productivity. FP 1 Even approaches that are based on Monitoring with cleaning and differial 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 aforemioned ones and to provide cleaning and for online, real-time conditional assessm. 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 operation optimisation. Water & Wastewater Asia December / January

4 PERFORMANCE 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 Pressure 67.1 bar 66.4 bar 66.1 bar 65.2 bar 65.9 bar Unit 4 Unit 3 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 Flow 680m3/hr 671m3/hr 669m3/hr 690m3/hr 685m3/hr Curr : 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 Oct 2009 Unit 6 667m3/h 691m3/h 684m3/h 667m3/h 691m3/h 684m3/h 21 Nov Jan 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 (Cli) 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 differ 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 repl. The performance monitoring function RO unit 2 RO unit 1 also allows an assessm 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

5 Figure 10 Exemplary Operator Screen used to pres membrane performance monitoring and optimisation RO Unit 1 RO Unit 2 RO Unit 3 RO Unit 4 Train F Train H Train H: RO Monitoring and Optimisation Solution Permeability Close Optimal Values Lest Calculation :99: Curr : Actual Set Point Optimal Product flow rate (m 3 /rr) 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 aforemioned, 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 implemed 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 fl exibility in terms of application, the modules are based on an ABB information managem system which is used for data handling, storage and information managem. This is outlined in Figure 9. The information managem 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 managem (see Figure 10). Results from the performance monitoring and optimisation solution are stored in the information managem system 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 managem system. Reports can be created in Microsoft Offi ce and can be deployed as html files on the web server of the information managem system the reports are accessible and viewable using thin cli technology. Successful implemation The implemation of the performance monitoring and optimisation solution has successfully been implemed 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 impleming 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 improvem potial by further impleming the suggested optimal set-points. Benefi ts 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 repl can be avoided. Besides all this, the plant availability is increased by lowering cleaning and repl activities and thus reducing plant downtimes. The solution is applicable to differ membrane configurations such as hollow fibre and can be used with existing or new installations without requiring additional measurems. Overall, with this newly developed approach, the maintenance process for membrane systems can be changed from reactive, previve 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 Shilmurugan S (ABB Corporate Research, Bangalore, India) and Mr Marc Antoine, (ABB Switzerland, business unit power generation, Baden, Switzerland). Water & Wastewater Asia December / January