Wor World Congress/Perth Convention and Exhibition Centre (PCEC), Perth, Western Australia September 4-9, 2011 REF: IDAWC/PER11-345 ANALYSIS OF COST OF WATER PRODUCTION FROM SEAWATER DESALINATION IN SAUDI ARABIA Authors: Saud Bin Marshad and Adebayo J. Adeloye Presenter: Saud Bin Marshad Operation Division Mgr Al-khobar — Saline Water Conversion Corp — Saudi Arabia. Abstract To cover the shortage of fresh water and to take advantage of the huge amount of saline water in the Earth’s seas, the idea of desalination arose. The Kingdom of Saudi Arabia (KSA) as well as many other countries started to produce the necessary drinking water from the sea by using different desalination techniques. For years, production costs of desalinated water have been declining as an outcome of technical improvements in the various technologies. Cost categorization is different from one organization to another but in general it includes capital cost and operation costs which can be divided into direct elements such as materials, labour, and fuel and indirect elements like depreciation, insurance, and shipment. In fact, various factors affect the cost of the production of a desalination plant which have to be considered during plant design period. This paper will discuss the role of seawater desalination plants in meeting the water resource needs in the Kingdom of Saudi Arabia. Data on the cost of water production from seawater desalination plants for ten years have been collected from East coast and West coast of the Kingdom Of Saudi Arabia, and analyzed to identify the main factors that affect the cost of desalination treatment operations. One of these factors, the production capacity (m 3 /day), was then used to develop and test several predictive models for the production cost of desalination water in the Kingdom of Saudi Arabia. The paper will summarise the data and present the result of calibrating, testing and validating the models.
Transcript
Wor World Congress/Perth Convention and Exhibition Centre (PCEC), Perth, Western Australia September 4-9, 2011 REF: IDAWC/PER11-345
ANALYSIS OF COST OF WATER PRODUCTION FROM SEAWATER DESALINATION IN SAUDI ARABIA Authors: Saud Bin Marshad and Adebayo J. Adeloye
Presenter: Saud Bin Marshad Operation Division Mgr Al-khobar — Saline Water Conversion Corp — Saudi Arabia. Abstract To cover the shortage of fresh water and to take advantage of the huge amount of saline water in the Earth’s seas, the idea of desalination arose. The Kingdom of Saudi Arabia (KSA) as well as many other countries started to produce the necessary drinking water from the sea by using different desalination techniques. For years, production costs of desalinated water have been declining as an outcome of technical improvements in the various technologies. Cost categorization is different from one organization to another but in general it includes capital cost and operation costs which can be divided into direct elements such as materials, labour, and fuel and indirect elements like depreciation, insurance, and shipment. In fact, various factors affect the cost of the production of a desalination plant which have to be considered during plant design period. This paper will discuss the role of seawater desalination plants in meeting the water resource needs in the Kingdom of Saudi Arabia. Data on the cost of water production from seawater desalination plants for ten years have been collected from East coast and West coast of the Kingdom Of Saudi Arabia, and analyzed to identify the main factors that affect the cost of desalination treatment operations. One of these factors, the production capacity (m3/day), was then used to develop and test several predictive models for the production cost of desalination water in the Kingdom of Saudi Arabia. The paper will summarise the data and present the result of calibrating, testing and validating the models.
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I. INTRODUCTION Shortage of conventional fresh water supplies is one of the most serious threats to the sustenance of life in arid regions where typically the average annual rainfall is less than 200 mm (Abdurrahman, 2005). In the Kingdom of Saudi Arabia (KSA) the average annual rainfall is less than 100 mm, making it an extremely arid country (Al Zawad, 2008). Meeting the freshwater needs of the teeming population is thus a big challenge. With a high average annual growth rate of 2.39%, the population in Saudi Arabia has increased rapidly from 7.7 million in 1970 to over 27 million in 2010 and it is expected to reach 45 million by 2050(UN A 2010, MEP 2011). As a result, water demand has increased sharply from 2,352 million m³ in 1980 to a peak of 31,696 million m³ in 1992 before falling dramatically to 14,100 million m³ in 2000 as shown in Figure 1. This decrease was due to new government interventions, notably more restrictions on well-drilling and the 75% reduction in the financial subsidy that farmers get for wheat cultivation. While the latter achieved an immediate reduction in water demand by farmers, its effect was, however, short lived and water demand has continued to rise steadily since the end of 2000, reaching 18,300 million m³ by the end of 2009 (Abdurrahman, 2010), representing more than 770% increase relative to the 1980 figure. With reference to the population growth forecast, KSA will be needing 30,346 million m³ by 2050, assuming that the average per capita consumption remains at its current level, or much more.
Figure 1: Water Demand in KSA
To solve the problem of drinking water shortage and as a strategic choice, the Kingdom of Saudi Arabia has started to cover the deficit in drinking water from seawater desalination plants. The first seawater desalination plant to be opened in Saudi Arabia was the Al-Wajh and Duba plant in 1969 with a production capacity of 198 m³/ day. Today, however, Saudi Arabia is producing more than 5.5 million m³/day of water from over 30 plants (Pankratz, 2009). It is worth mentioning that more than 50% of the total domestic water demand is sourced from seawater desalination plants (Abdulraof, 2009), but this is bound to increase in the future in line with the rapid increase in demand and population. The problem with this, however, is the high cost of water from these plants. While technology advances are causing
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the cost to come down, further reductions in cost of production could be possible by more adept decisions on operation and maintenance costs, the two main elements of the total unit cost, if the factors contributing to them are better understood. 1.1 Aim and Objectives The aim of this work was to understand the factors that determine the cost of water production at desalination plants in Saudi Arabia and to use these to develop a predictive model for production cost. The specific objectives are to:
1. Collect data – capacity, cost, history, type of technology, raw water quality, etc.- on existing desalination plants in Saudi Arabia;
2. Carry out exploratory correlation analysis with a view to identifying those factors that are statistically significant for the production cost;
3. Formulate different regression models for predicting the production cost using the identified factors as explanatory variables;
4. Calibrate, test and validate the predictive models and make recommendations. When successfully implemented, the predictive model will constitute a valuable decision support tool for operations and maintenance decision-making for seawater desalination plants in Saudi Arabia. II. DESALINATION TECHNOLOGIES
To meet the shortage in fresh water and to take advantage from the huge salinity water in the Earth, the idea of desalination idea arose. Desalination refers to the treatment of water that removes the salt from it. A great variety of types of desalination exist worldwide, all of which produce water which is suitable for drinking and other municipal purposes. Salinity of feed water is one of the most important factors in the selection of desalination technology. There is generally high variation in salinity between the seawater (TDS> 10,000) and brackish water (1500< TDS<10,000). Moreover, the salinity as well as other chemical contents of brackish water and seawater depends on regional location, for example, the Baltic Sea's average TDS is around 10,000 ppm, while in the Arabian Gulf the average salinity is much higher, about 48,000 ppm (Micale, 2009). Consequently, there are two major types of desalination - seawater desalination, and brackish water desalination process. The seawater desalination process is generally more expensive than the brackish water desalination process since the seawater needs more treatment and purification than brackish water (Al-Sofi (a), 2001, Greenlee et al, 2009) A large number of desalination technologies are applied commercially. There are two main categories of the main processes. The first is a thermal process which is a physical separation by converting the water in seawater to vapor by heat energy which mostly steam. The two techniques of multi-stage flash (MSF) and multi effect desalination (MED) use this thermal principle. It is worth mentioning that the thermal desalination process can work in a wide range of intake seawater temperatures and salinity (Ettouney-Wilf, 2009). The second technology group is the membrane process, which operates on the difference in size between the molecules of water and molecules of salt. It is also a physical process but uses either hydraulic energy or electrical energy to separate fresh water from high salinity water through a membrane. Included in this process category are the Reverse Osmosis (RO), Electrodialysis (ED), and Electrodeionization (EDI).
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The three desalination technologies have been applied commercially in seawater desalination plants in the KS A are MSF, MED, and RO. Of these, the MSF is the most common, accounting for over 70% of the total desalination water production Figure 2. One of the successful methods to reduce the overall costs of desalination product is the use of hybrid desalination which combines two desalination techniques in the same plant (Buros, 2000). In the Kingdom of Saudi Arabia, the Jeddah and Al-Jubail have for a long time used hybrid desalination by combining MSF and RO for example. Other successful methods include co-generation or dual purpose plants which have been successfully applied in the KSA and other Gulf Cooperation Council (GCC) countries to run desalination units along with a power plant and through this reducing overall production cost (Hamed, 2005, Buros, 2000, Al-sofi(b) et al, 2000, Al-Mutaz- Al-Namlah, 2004).
Figure 2: Installed capacity by desalination technology in Saudi Arabia seawater desalination plants (Pankratz, 2009, SWCC, 2010)
III. COST OF DESALINATION For years, production costs of desalinated water have been declining as an outcome of technical improvements in various technologies. Furthermore, there are other methods which have been used to reduce the cost of the desalination plant production such as hybrid desalination or the co-generation principle. Cost categorization is different from one organization to another but in general it includes capital and operation costs. Operation costs can be divided into direct elements such as materials, labour, and fuel, and indirect elements like depreciation, insurance, and shipment (Frioui- Oumeddour, 2008). Various factors affect the cost of the production of a desalination plant. The most important of these factors are plant capacity, feed water, energy consumption and its cost, and the type of desalination techniques. Other factors include plant location and space requirements, manpower qualifications and cost, plant life and reliability, operation and maintenance aspects, financing, and disposal treatment
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(Buros, 2000; Al-Sahali- Ettouney, 2006; Al-Subaie, 2006; Khawaji (b) et al.,2008; Karagiannis and Soldatos, 2008, Ettouney- Wilf, 2009). As a result of the above multiplicity of factors, the total cost of distillate water produced from seawater desalination plants in Saudi Arabia variable is highly variable from year as in any other industry. For example as shown in Figure 3, the average annual cost in ten years increased from 2.49(SR/m³) in 2000 to 2.57 (SR/m³) in 2009, with a minimum of 2.23 SR/m3 in 2006. This observed trend is due to different reasons. The first reason is water production, which increased dramatically from 2.08 million m³ per day in 2000 to 2.93 million m³ per day in 2004 as a result of the commissioning of new plants at Shouba3, Khobar3, and Jubail RO. However, this increased capital cost was offset by economy of scale achieved in the operation and maintenance cost causing the total cost to decrease from 2.49 (SR/m³) in 2000 to 2.25 (SR/m³) in 2004. The second reason that effected the distillate water cost is end life time of some plants like Khobar3, and Jubail2 in 2008 which reflected on reduction in capital cost (installation cost) as shown in Figure 3. Finally, due to the rehabilitation and life extension for existing plant as well as the development projects that have been applied in Saline Water Conversion Corporation (SWCC), which is the organization that operate seawater desalination plant in Saudi Arabia, the maintenance cost as well as the administrative cost went up within five years from 0.29, and 0.26 (SR/ m³) in 20004 to 0.51 and 0.5 (SR/ m³) in 2009 respectively. It is clear from Figure 3 that operation cost is a significant component of the total cost and anything that causes this to decrease significantly will cause a significant reduction in the total cost.
Figure3: The distribution of costs of distilled water from seawater desalination plants in KSA (SWCC, 2000-2009) IV. METHODOLOGY 4.1 Data collection
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The main seawater desalination plants in the Kingdom of Saudi Arabia whose production capacity is more 50,000 M³/ day have been selected for the existing research. These plants are Jubail, Khobar, Jedah, Shouba, Yanba and Shoqiq, all under the Saline Water Conversion Corporation (SWCC) management which is the main organization responsible for operating and maintaining the seawater desalination plants in the Kingdom of Saudi Arabia. The annual report of maintenance and operation sector of SWCC for ten years (2000- 2009) was the main source of the data used in this research, giving a total of 60 data points. The data have been rigorously subjected to quality checks by the SWCC although as will become clearer later, additional statistical tests will be carried out to identify any outliers and other abnormal values before using the data for model development. The collected data are summarised in Table 1. While several factors have been identified previously as affecting the production cost, only data relating to the production capacity were available for this study as shown in Table 1. Consequently, the production capacity formed the sole input factor or variable for the predictive models. Table1: Summary of the data collected
Shoqiq 97,014 1989 1.026 0.347 0.608 0.562 2.544 MSF * Different phases and different year commissioned
4.2 Data pre-processing Pre-processing carried out was aimed at establishing the existence of outliers in the data and removing them before further analyses. For this purpose, the z-score approach was used, where the z-score is defined as:
( )σµ−
=X
Z (1)
Where X is the observation,
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Regression models were postulated using the production capacity as the only explanatory variables as follows: Linear Y = a + ( X) (2)
Logarithmic Y= a + ( ln X) (3)
Quadratic Y= a + X + X² (4)
Cubic Y= a + X + X² + X³ (5)
Compound Y= a (6)
Power Y= a (7)
Inverse Y= a + ( / X) (8)
S-curve Y= (9)
Growth Y= (10)
Exponential Y= a (11) where Y is the production cost (Million SR) and X is the water production (m3/day), and a, , , and
are regression coefficients to be estimated by calibration (Harrell,2001; Field,2009). The calibration was carried out SPSS software tool. V. RESULTS AND DISCUSSION 5.1 Pre-processing The mean and standard deviation of the production cost were 2.37 and 0.447 respectively. When applied in equation (1), it was found that the Khobar plants 2000 cost of 4.24 SR/ M³ has low probability of occurring of 0.003% compared to other readings which are unlikely to occur. Because of this result, this reading has been excluded, leaving 59 measurements for the models development. Figure 4 compares the frequency distribution for the complete set and the set with the outlier removed from where it is clear that the removal of the outlier has produced a near-normally distributed data set when compared with the skewed complete data set. Normality assumption is necessary for the subsequent regression analysis.
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Figure 4: Frequency distribution diagram for the total unit cost (a) left: raw data (b) right: with outlier removed 5.2 Regression Models Calibration The collected data set was split into two parts, with one part being used for model calibration and the other for model validation. Two approaches were employed for selecting the two sets: the first is sequential in which the first 40 observations were used for calibration and the remaining 19 used for validation. The second approach randomly selects the 40 data records to comprise the calibration set, leaving the remainder for validation. Results for both approaches are presented and discussed. Table 2 summarises the result of the sequential method during calibration where it is clear that the statistically significant test (sig.) probability in all models is less than 0.05, which means that the null hypothesis that the regression parameters are zero can be rejected. The performance of the models in fitting the data are also shown in Figure 5 from where it is clear that of all the models postulated, three- the compound, growth and exponential (see equations 12-14) appear to fit the data better than the rest. The evidence provided by the plots in Figure 5 is also supported by the coefficient of determination (R²) which is at 0.439 is the highest for all the models calibrated. Furthermore, the obtained (F-ratio) for these three models is the highest at 29.794.
Table 2 :Models summary and parameter estimates in the sequential method
Equation
Model Summary Parameter Estimates R Square F df1 df2 Sig. Constant b1 b2 b3
Linear 0.408 26.18 1 38 0.000 2.729 -0.633
Logarithmic 0.39 24.31 1 38 0.000 2.104 -0.375
Inverse 0.335 19.18 1 38 0.000 1.969 0.176
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Where the dependent Variable is total Production Unit Cost (SR/ m³) and the independent variable is Average daily production (m³/ Day).
Figure 5: The regression models during calibration (sequential method)
Compound Y= 2.745 (12) Growth Y= (13) Exponential Y= 2.745 * (14) The three models were validated using the remaining 19 data sets and the result of the validation is summarized in Table 3. Figure 6 is the X-Y scatter plot of the observed and predicted unit cost during validation. Table 3: Comparing the results of three models with real data during validation (sequential method) Year plant Ave. daily
production Million(m³/
Total unit cost (SR/ m³) Real
Total unit cost (SR/ m³)
Total unit cost (SR/ m³)
Total unit cost (SR/ m³) Exp.
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Figure 6: X-Y scatter plot of observed and predicted cost during validation (sequential method)
The calibration results of the random method are summarized in Table 4. As was the case in the sequential method, the statistically significant test (sig.) in all models is less than 0.05 probability, which means that the null hypothesis that the regression parameters are zero can be rejected. However, the models here are slightly poorer than their sequential counterparts based on both the R2 and F-statistics criteria. Additionally, the three best performing models for the random method were Linear, Quadratic
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and Cubic (equations15-16). The coefficient of determination (R²) for these models are 0.35, 0.372, 0.566 respectively and Figure 7 shows the fitted curves, which further demonstrate the superiority of the three models (equations 15 – 16) over the others.
Table 4 :Model Summary and Parameter Estimates in the random method
Equation
Model Summary Parameter Estimates R Square F df1 df2 Sig. Constant b1 b2 b3
0.334 19.06 1 38 0 2.691 -0.303 Where the dependent Variable is total Production Unit Cost (SR/ M³) and the independent variable is Average daily production (M³/ Day).
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Figure 7: The regression models during calibration (random method)
Linear Y= 2.694 + (-.696 X) (15) Quadratic Y= 2.851 + (-1.466 X) + 0.632 X² (16) Cubic Y= 2.259+ 4.442 X + (-13.319 X²) + 8.45 X³ (17) The results of validating the models for the random method are summarized in Table 5 where it is clear that the three best models are adequately suitable for predicting the unit cost of production. The X-Y scatter plot of observed and predicted unit cost is also shown in Figure 8; it is easy to note in Figure 8 that the cubic model is the closest model to the real data, which agrees with the R² criterion as presented earlier. Table 5: Comparing the results of three models with real data (random method)
Figure 8: X-Y scatter plot of observed and predicted cost during validation (random method)
VI. CONCLUSION With a high average annual growth rate of population in Saudi Arabia and the rapid increase in water demand in Saudi Arabia with limited sources of water, a reliance on desalination of sea water as a source of water has become inevitable. However, the high cost of the product is a problem that must be met. Along with technical development and the great contribution in reducing the cost of the product, there must be a development in desalination plant management and ways to benefit from past experiences to reduce the cost of production by linking different factors or phenomena and the final cost of production in each geographical area.
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In this study, the relationship between the average daily production and the total unit cost of the main seawater desalination plant in the Kingdom of Saudi Arabia from 2000 to 2009 has been analyzed. As a final conclusion of the analysis process, a nonlinear cubic model proved to be the most effective for predicting the cost of production using the production capacity as the independent variable. Other factors also play a role in determining the cost of production that could form additional input variables but data on these were not readily available at the time of the analysis reported in this work. The next stage of the work therefore is to collect these and other data which will then be used for calibrating more complete models for predicting production cost of desalination water in Saudi Arabia.
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