The five most commonly used concentrate management alternatives that considered in this research are: 1) surface water discharge; 2) sewer disposal; 3) deep-well injection; 4) evaporation pond; and 5) land application. Cost analysis of each option was carried out where the best option was selected. The cost analyses considered mainly the capital and the expected operation and maintenance costs.

MCA is both an approach and a set of techniques, with the goal of providing an overall ordering of options, from the most preferred to the least preferred option. The options may differ in the extent to which they achieve several objectives, and no one option will be obviously best in achieving all objectives. In addition, some conflict or trade-off is usually evident amongst the objectives, options that are more beneficial are also usually more costly, for example. Costs and benefits typically conflict, but so can short-term benefits compared to long-term ones, and risks may be greater for the otherwise more beneficial options. MCA is a way of looking at complex problems that are characterized by any mixture of monetary and non-monetary objectives, of breaking the problem into more manageable pieces to allow data and judgments to be brought to bear on the pieces, and then of reassembling the pieces to present a coherent overall picture to decision makers. The purpose is to serve as an aid to thinking and decision making [10] . The steps for MCA in this research are:

1) Establish aims of the MCA, and identify decision makers and other key players. The aim of the MCA in this research to get the best option for brine disposal. Academic experts and experts in the subject of research are invited to participate in MCA.

2) Identify the options to be appraised where choose four options of brine disposal were selected as:

- Pump brine to sea water.

- Deep well injection.

- Evaporation pond.

- Irrigation.

3) Four criteria were used in this research: economic which took 30%, Environmental took 35%, technical took 20%, social and political took 15%. The criteria and its load were suggested by academics and experts persons.

4) Each option were rated and assessed against the four criteria. Then assess the value associated with the consequences of each option for each criterion, also rating put by academics and experts persons. The scale for these rating is from 1 to 5.

5) Assign weights (Weighting) for each of the criterion to reflect their relative importance to the decision. Weights put by academics and experts persons, the scale in weighting from is from 1 to 3.

6) Combine the weights and rating for each option to derive an overall value and then examine the results.

The Analytical Hierarchy Process (AHP) is a decision-aiding method developed by Saaty. It aims at quantifying relative priorities for a given set of alter-natives on a ratio scale, based on the judgment of the decision-maker, and stresses the importance of the intuitive judgments of a decision-maker as well as the consistency of the comparison of alternatives in the decision-making process [11] . Since a decision-maker bases judgments on knowledge and experience, then makes decisions accordingly, the AHP approach agrees well with the behavior of a decision-maker. The strength of this approach is that it organizes tangible and intangible factors in a systematic way, and provides a structured yet relatively simple solution to the decision-making problems [12] .

In addition, by breaking a problem down in a logical fashion from the large, descending in gradual steps, to the smaller and smaller, one is able to connect, through simple paired comparison judgments, the small to the large.

The following steps were followed for applying AHP:

1) Define the problem and determine its goal.

2) Structure the hierarchy from the top (the objectives from a decision-makers viewpoint) through the intermediate levels (criteria on which subsequent levels depend) to the lowest level which usually contains the list of alternatives.

3) Construct a set of pair wise comparison matrices (size n × n) for each of the lower levels with one matrix for each element in the level immediately above by using the relative scale measurement shown in Table 1. The pair-wise comparisons are done in terms of which element dominates the other.

4) n(n-1) judgments required to develop the set of matrices in step 3. Reciprocals are automatically assigned in each pair wise comparison.

5) Hierarchical synthesis is now used to weight the eigenvectors by the weights of the criteria and the sum is taken over all weighted eigenvector entries corresponding to those in the next lower level of the hierarchy.

6) Having made all the pair-wise comparisons, the consistency is determined by using the Eigen value, , to calculate the consistency index, CI as fol-lows:

where:

n is the matrix size. Judgment consistency can be checked by taking the consistency ratio (CR) of CI with the

appropriate value (RI) in Table 2.

The CR is acceptable, if it does not exceed 0.10. If it is more, the judgment matrix is inconsistent. To obtain a consistent matrix, judgments should be reviewed and improved.

7) Steps 3 and 6 are performed for all levels in the hierarchy. Then, the following can be done manually or automatically by the AHP software, Expert Choice:

- synthesizing the pair-wise comparison matrix.

- calculating the priority vector for a criterion such as experience.

- calculating the consistency ratio.

- calculating.

- calculating the consistency index, CI.

- selecting appropriate value of the random consistency ratio from Table 2.

- checking the consistency of the pair wise comparison matrix to check whether the decision makers comparisons were consistent or not.

The construction cost, operation and maintenance cost for each option were obtained and presented in Table 3.

The results of each one of the eight experts, academic and professionals, are obtained for four alternatives: surface discharge, deep well injection, evaporation pond and finally the irrigation. Table 4 shows the average weights and rates for the eight experts for the same four alternatives. In such that type of analysis, the options were reduced to four rather than five options, where the second option was excluded (Discharge brine to wastewater plant) because the amount of brine was very large and the plant couldn’t accommodate that amount.

Figure 1 indicates the results of GMA and the options can be ordered as follows:

1) Pump to seawater (P.S) that obtained 70.8%.

2) Deep well injection (D.W) that obtained 47%.

3) Evaporation pond (E.P) that obtained 55.7%.

4) Irrigation (IR) that obtained 47.6%.

It can be clearly seen that the best option is brine disposal to the sea which got 70.8%

The results of the calculations for these AHP was explained for illustration purposes. Synthesizing the pair-wise comparison matrix was performed by dividing each element.

Table 5 indicates the weight for every option with regard to criteria of economic cost, where these weights have been set by experts. For example, A take regard to B in economic cost 3 while B regard to A take 1/3.

This process will be repeated in Tables 6-10 but with different criteria. Of the matrix by its column total. For example, the value 0.08 in Table 6 is obtained by dividing 1 from Table 5 by 12.5, which the sum of column items in Table 5 (1 + 3 + 2 + 6 + 1/2) = 12.5.

The priority vector in Table 6 can be obtained by finding the row averages. For example, the priority of option A with respect to the criterion “experience” in Table 6 is calculated by dividing the sum of the rows (0.08, 0.08, 0.07, 0.08, 0.12) by the number of options (columns), i.e., 5, in order to obtain the value 0.09. The priority vector for experience, indicated in Table 6.

Now, estimating the consistency ratio is as follows:

where (0.09, 0.25, 0.15, 0.46 and 0.06) from Table 6 and these matrices from Table 5. Dividing all the elements of the weighted sum matrices by their respective priority vector element, we obtain:

We then compute the average of these values to obtain:

Now, we find the consistency index, CI, as follows:

Selecting appropriate value of random consistency ratio, RI, for a matrix size of five using Table 2, we find that RI = 1.12. Then the consistency ratio is calculated (CR) as follows:

As the value of CR is less than 0.1, the judgments are acceptable. Similarly, the pair-wise comparison matrices and priority vectors for the remaining criteria can be found in Tables 7-15, respectively. Table 7 and Table 8

presented the results of environmental criteria were:, CI = 0.08, RI = 1.12. It can be noticed that CR is less than 0.1 which can be accepted.

Table 9 and Table 10 presented the results of technical criteria were:, CI = 0.06, RI = 1.12, and CR is 0.05 which is less than 0.1 and it can be accepted. Table 11 and Table 12 presented the results of political criteria were:, CI = 0.06, RI = 1.12, and CR is 0.05 which is less than 0.1 and it can be accepted.

Table 13 and Table 14 presented the results of political criteria were:, CI = 0.07, RI = 1.12, and CR is 0.06 which is less than 0.1 and it can be accepted.

In addition to the pair-wise comparison for the decision alternatives, we also use the same pair-wise comparison procedure to set priorities for all five criteria in terms of importance of each in contributing to the overall goal. Table 15 and Table 16 show the pair-wise comparison matrix and priority vector for the five criteria. The results are: is 5.20, CI is 05, RI is 0.12 and CR is 0.04 which is less than 0.1 and its also can be accepted.

Manually combine the criterion priorities and the priorities of each decision alternative relative to each criterion in order to develop an overall priority ranking of the decision alternative which is termed as the priority matrix Table 17. The overall priority of option A is obtained as follows:

After that the options are ranked according to their overall priorities values as follows: D, C, A, B, and E, indicating that D is the best option.