Alex R. Bartman

Rapid field assessment of RO desalination of brackish agricultural drainage water.

Rapid field evaluation of RO feed filtration requirements, selection of effective antiscalant type and dose, and estimation of suitable scale-free RO recovery level were demonstrated using a novel approach based on direct observation of mineral scaling and flux decline measurements, utilizing an automated Membrane Monitor (MeMo). The MeMo, operated in a stand-alone single-pass desalting mode, enabled rapid assessment of the adequacy of feed filtration by enabling direct observation of particulate deposition on the membrane surface. The diagnostic field study with RO feed water of high mineral scaling propensity revealed (via direct MeMo observation) that suspended particulates (even for feed water of turbidity <1 NTU) could serve as seeds for promoting surface crystal nucleation. With feed filtration optimized, a suitable maximum RO water recovery, with complete mineral scale suppression facilitated by an effective antiscalant dose, can be systematically and directly identified (via MeMo) in the field for a given feed water quality. Scale-free operating conditions, determined via standalone MeMo rapid diagnostic tests, were shown to be applicable to spiral-would RO system as validated via both flux decline measurements and ex-situ RO plant membrane scale monitoring. It was shown that the present approach is suitable for rapid field assessment of RO operability and it is particularly advantageous when evaluating water sources of composition that may vary both temporally and across the regions of interest.

Control of a reverse osmosis desalination process at high recovery

Feed-forward/feedback control techniques that utilize Lyapunov-based control laws are implemented on a high recovery reverse osmosis desalination plant model. A detailed mathematical model of a high recovery reverse osmosis plant is developed. This model incorporates the large spatial variations of concentration and flow-rate that occur in membrane units during high recovery operation. Bounded nonlinear feedback and feed-forward controllers are developed and applied to this system. The application of these controllers is demonstrated in the context of a high recovery reverse osmosis process simulation. The scenarios demonstrate the ability to compensate for the effects of large time varying disturbances in the feed concentration on specific process outputs with feedforward/feedback control.

Model predictive control of feed flow reversal in a reverse osmosis desalination process

Model-predictive control algorithms are applied to a high capacity reverse osmosis (RO) membrane desalination process simulation that utilizes feed flow-reversal in order to prevent and/or reverse scale crystal formation on the membrane surface. A dynamic non-linear model which incorporates feed concentration and membrane properties is used for simulation and demonstration of optimally controlled feed flow reversal. Before flow reversal can take place on a high capacity RO plant, the flow into the membrane unit must be carefully reduced to eliminate the risk of membrane module damage and unnecessary energy consumption. A cost-function is formulated for the transition between the normal high flow steady-state operating point to a low flow steady-state operating point where it is safe to reverse the flow direction. Open-loop and closed-loop simulations demonstrate non-linear model-predictive control strategies that induce transition from the high-flow to low-flow steady-states in an optimal way.

Minimizing energy consumption in reverse osmosis membrane desalination using optimization-based control

This work focuses on the design and implementation of an optimization-based control system on an experimental reverse osmosis (RO) membrane water desalination process in order to facilitate system operation at an energy optimal condition. A dynamic nonlinear lumped-parameter model for the RO process is derived using first principles and the model parameters are computed from experimental data to minimize the error between model predictions and actual system response. This model, along with several equations for reverse osmosis system energy analysis, are combined to form the basis for the design of a nonlinear optimization-based control system. The proposed control system is implemented on UCLAs experimental RO system and its energy optimization capabilities are evaluated.