Saline water conversion--Reverse osmosis process

This study evaluated the technical feasibility o f increasing the typical water
recovery of a pilot scale membrane system (85-90%) to 97% by treatment of
nanofiltration concentrate with low-pressure reverse osmosis. The study used Biscayne
aquifer water (freshwater), and determined that it may be technically feasible to increase
the recovery up to approximately 95% when the RO flux is —10 gfd, the feed water pH is
reduced to -6.1 with H2 SO4 , and antiscalant in the NF process. The tested membranes
showed stable and similar performance under the pilot conditions. However, pilot tests
were sensitive to pH variations (pH>6.2). The main barrier for increasing the water
recovery was fouling caused by iron, carbonate hardness, and iron bacteria. A
preliminary cost analysis showed that there is an apparent econom ic advantage when the
recovery is greater than 90%. Estimated water cost at 95% recovery is $1.99 compared
with $2.69 at the typical 85% recovery.

A computer program was developed to simulate and optimize the chemical pretreatment of seawater prior to desalination by reverse osmosis. The model was created using LabViewRTM programming language. The automation of the process was achieved using a PID (proportional, integrative, derivative) controller. The effects of a variety of operating conditions were modeled to optimize the chemical pretreatment. We focused on three parameters: hardness removal, iron removal and control of biogrowth. The validity of the model was verified with laboratory scale experiments. In the range of the model, the predicted values differ by a maximum of 11% from experimental results.

Modeling of two reverse osmosis plants at FAU Gumbo Limbo facility and at the city of Boca Raton are investigated. System identification as well as artificial neural networks are utilized to carried out the tasks. The data for a six months operational period of both plants are utilized. The prediction error method and subspace method are utilized to estimate state-space model while the auto regression with extra input (ARX) model is estimated by using the least square method and the approximately optimal four-stage instrumental variable method. The training algorithms for artificial neural networks are based on backpropagation and radial basis network function (RBNF). The implementation of each methodology is performed step by step and finally, the results from both methodologies are analyzed and discussed. The results of the proposed study indicate that both system identification and neural networks algorithms can predict the outputs of both RO plants with the acceptable accuracy. Among all methodologies utilized in the thesis, the least square method for the auto regression with the extra input (ARX) model, can provide the best prediction for both RO plants.

This dissertation presents the design, implementation and application of soft computing methodologies to Reverse Osmosis (RO) desalination technology. A novel intelligent control scheme based on the integration of Neural Network (NN) and Fuzzy Logic (FL) is presented to optimize plants' performance. In the first part of the research work, two optimal NN predictive models, based on backpropagation and Radial Basis Function Networks (RBFN), were developed for three types of RO feed intakes. The predictive models utilized actual operating data for the three RO plants in order to predict system recovery, total dissolved solids and ion product concentration in brine stream A predictive model is proposed based on redistributed receptive fields of RBFN. The proposed algorithm utilizes integration of supervised learning of centers and unsupervised learning of output layer weights. Extensive simulations are presented to demonstrate the effectiveness of the proposed method for generalization on prediction of nonlinear input-output mappings. In the second part of the study, the design of FL control strategy for direct seawater RO system is carried out. The real-time controller design is based on integration of sensory information, predicted outputs, mathematical calculations, and expert knowledge of the process to yield a constant recovery, constant salt rejection and minimum scaling under variable operating conditions. To implement the designed methodology, a 250/800 Gallon per Day (GPD) prototype RO plant with direct Atlantic Ocean intake is constructed at FAU Gumbo Limbo research laboratory. Two types of membrane modules were used for this study: Spiral Wound (SW) and Hollow Fine Fiber (HFF). The prototype plant indeed demonstrated the effectiveness and optimum performance of the proposed design under variable operating conditions. The system achieved a constant recovery of 30% and salt passage of 1.026% while ion product concentration for six major salts were kept below their solubility limits at all time. The implementation of the proposed intelligent control methodology achieved a 4% increase in availability and a 50% reduction in manpower requirements, as well as reduction in overall chemical consumption of the plant. Therefore, it is expected that the cost of producing fresh water from seawater desalination will be decreased using the proposed intelligent control strategy.