An atomic force microscope study of calcium carbonate adhesion to desalination process equipment: effect of anti-scale agent

The present study is applied on the seawater multi-stage flash (MSF) desalination plant that is currently under operation in E-Zuetina operations plant located in Libya. The plant contains 21 evaporator stages at capacity of 10025 (ton/day). The presented operating data has been collected during a visit of the plant, a mathematical model for multistage flash (MSF) desalination plants was developed. The model was based on basic principles of physics and chemistry that describe the stages occurring in the desalination process. The input plant parameters that are known to affect the operation of the MSF desalination plant and its performance were taken into account in the construction of the model. These parameters included make-up flow, brine recycle flow, seawater flow, seawater temperature, seawater concentration, steam temperature and the plant load. For each stage, the developed model was used for predicting the temperatures and pressure of the brine, distillate, cooling brine, and the flow rates of brine outlet and distillate production. The developed model was evaluated with the MSF plant vendor simulation results and its actual operating data. The evaluation indicated that model predictions matched well with the vendor simulation results and the plant operating data. The developed model is sufficiently accurate and model predictions can be relied upon. Therefore, it may be recommended for determining optimum set point of a running MSF desalination plant at different loads to maximize the water production or minimize energy consumption. It can also be used to calculate controller set points for different loads of the plant. Keywords:E-Zuetina MSF Desalination Plant, case study, stage model, brine heater.

Multi stage flash desalination plant with brine–feed mixing and cooling

In this paper, a mathematical model for multistage flash (MSF) desalination plants was developed. The model was based on basic principles of physics and chemistry that describe the stages occurring in the desalination process. The input plant parameters that are known to affect the operation of the MSF desalination plant and its performance was taken into account in the construction of the model. These parameters included make-up flow, brine recycle flow, seawater flow, seawater temperature, seawater concentration, top brine temperature (TBT), steam temperature and the plant load. For each stage, the developed model was used for predicting the temperatures of the brine, distillate and cooling brine, and the flow rates of brine outlet and distillate production. The developed model was evaluated with the MSF plant vendor simulation results and its actual operating data. The evaluation indicated that model predictions matched well with the vendor simulation results and the plant operating data. The developed model is sufficiently accurate and model predictions can be relied upon. Therefore, it may be recommended for determining optimum set point of a running MSF desalination plant at different loads to maximize the water production or minimize energy consumption. It can also be used to calculate controller set points for different loads of the plant. Copyright © 2011 John Wiley & Sons, Ltd.

Thermal desalination technologies are very energy intensive. The utilisation of nuclear energy for seawater desalination provides a safe, feasible and economic solution for the production of very good quality water. The Multi-Stage Flash (MSF) desalination plant of the Nuclear Desalination Demonstration Project (NDDP) of the Department of Atomic Energy (DAE), Government of India, is coupled with a nuclear power plant on the south east coast of India to share the common facilities and steam. The MSF desalination plant is under construction. This paper describes a case study of the coupling aspects of the MSF desalination plant with the existing nuclear power plant and gives an estimate of the loss of electrical power generation due to extraction of steam. Loss of electrical power is also compared with the Desalination Economic Evaluation Program (DEEP) of the IAEA.

Neural networks for the identification of MSF desalination plants

The chemically aggressive environment generated in some parts of equipment at multi-stage flash (MSF) desalination plants can cause corrosion problems. The proper selection of materials with higher resistance to corrosion is considered as one of the most prospective approaches for smooth and efficient running of the plants. Because of this, the study of the corrosion behavior of selected materials is an important issue in the realm of desalination technology. This paper reviews the performance of materials used in different MSF desalination plants. The corrosion behavior of materials in different sections of plants, under surrounding environmental conditions, is discussed. Various types or forms of corrosion occurring in different units of plant are described and the strong role of local attack is emphasized. Case histories dealing with failure of components in different plants are cited. The criteria for the selection of materials, which depend upon the nature of environment and operating conditions, are exemplified. The merits and demerits of materials currently employed are highlighted and introduction of new materials either in existing plants as the possible replacements or in future plants are discussed.

This article presents the results of an investigation on the corrosion of flash chamber floor plates in a multistage flash (MSF) desalination plant. In an MSF plant, desalinated water is produced by flashing deaerated seawater in successive flash chambers under reduced pressure. The flash chamber floor plates were made of carbon steel with AISI type 317L stainless steel (UNS S31703) internal cladding. The thickness of the carbon steel and cladding was 8.5 and 3 mm, respectively. Approximately four years after the plant was commissioned, indications of corrosion processes, in the form of numerous red-colored spots, were noticed on the floor plates.

Techno-economical simulation and study of a novel MSF desalination process

The inorganic scaling in wells is a common problem faced by mining companies. At present, the use of protective coatings for tubing as a measure to prevent or reduce the formation of inorganic scale deposits on pipe walls has not been fully studied. To use protective coatings as a measure to counteract the deposition of inorganic salts, it is necessary to develop a method that allows assessing the ability of coatings, as well as polymer and metal materials, to prevent the formation of inorganic scale deposits on the inner surface of pipes.The article proposes a method for assessing the ability of protective coatings to resist the inorganic scaling on the inner surface of tubing. The proposed assessment method allows to make an informed decision on the advisability of using internal protective coatings of tubing to prevent (or reduce) the formation of inorganic scale deposits. The authors consider design features of a test bench for assessing the resistance of coatings to inorganic scale deposits, which allows to simulate the conditions for the formation of scale deposits that are as close as possible to the real conditions of oil production facilities. The article presents the results of bench tests of nine coating samples, two polymer samples and one sample made of St 40G2 steel. To assess the effectiveness of using tubing with an internal anti-corrosion coating as a measure to combat scale deposits, additional research is required to assess the possibility of complex use of coatings in conjunction with other methods of preventing processes of inorganic scaling. Thus, the authors developed the Bench for assessing the resistance of protective coatings of tubing to inorganic scale deposits. A dynamic testing technique is proposed to evaluate the resistance of protective coatings to inorganic scale deposits. Based on the presented results, conclusions were drawn about the possibility of using protective coatings on tubing as a measure to prevent the formation of inorganic scale deposits on the inner surface of the tubing.

Novel multi-stage flash (MSF) desalination plant driven by parabolic trough collectors and a solar pond: A simulation study in UAE

Highly efficient corrosion inhibitor for C1020 carbon steel during acid cleaning in multistage flash (MSF) desalination plant

Over the past few decades, the growth of the earth’s population has increasingly contributed to global warming, and increased demand for fresh water on top of a need for a greater power supply. This paper will analyze the integration of a combined- gas turbine with intercooler and a multi-stage flash (MSF) desalination plant for the simultaneous generation of electricity and supply of water and improved performance. The paper will also examine the calculation of the production cost and the capital cost of the MSF desalination plant. The thermo-economic analysis of the study was conducted using the IPSEpro software system. Also, exergy losses of the gas turbine and MSF desalination unit were also calculated. The desalination plant uses the exhaust gases from the gas turbine, as a form of thermal energy, and discharges to feed the seawater heater. A portion of the desalinated water is used to cool the compressed air in the intercooler heat exchanger. Results indicate the improvement in the gas turbine’s performance when desalinated water is used for the intercooler between compressor stages, as the power output increased by 59% over the the simple gas cycle and found decrease with an increase in the ambient temperature. The desalinated water cost was calculated to be $1.7 per cubic meter after determining the optimal configuration and operating conditions. A decrease in steam cost by 36% was observed when the waste heat from the gas turbine was used as the source of thermal energy. Part of this reduction can be explained by the observation that the desalination plant’s pumps consume part of the power generated by the gas turbine.

Fault diagnosis for an MSF desalination plant by using Bayesian networks

Studies by simulation on the model of a multistage flash (MSF) desalination plant have shown certain features of the model developed, on the basis of physical laws and correlations. The most important of these is nonlinearity which relates to the changing of the process characteristics with the operating conditions. It is shown that a fixed PID controller cannot be optimal, if the operating point changes from the one at which the optimal controller was designed. In this paper, the parameter variations in the linearised plant model have been modelled over the operating region of interest, and these are mapped into the parameter space of the optimal PID controller. The resulting algorithm is a parameter scheduling scheme for the MSF plant for optimal PID control with a choice among four criteria (ISE, IAE, ISTE, and IATE). The scheme illustrated is with reference to the top brine temperature control of an actual 18 stage MSF desalination plant.

This paper presents a methodology and practical guidelines for developing predictive models for large-scale commercial water desalination plants by (1) a data-based approach using neural networks based on the backpropagation algorithm and (2) a model-based approach using process simulation with advanced software tools ASPEN PLUS and SPEEDUP and compares the relative merits of the two approaches. This study utilizes actual operating data from two of the largest multistage flash (MSF) and reverse osmosis (RO) desalination plants in the world. Our resulting neural network and process simulation models are capable of accurately predicting the actual operating data from commercial MSF desalination plants, but the accuracy of a neural network model depends on both the proper selection of input variables and the broad range of data with which the network is trained. A neural network model can handle noisy data more effectively than statistical regression and performs better in predicting the performance variables of both MSF and RO desalination plants. Our neural network model compares favorably with recent neural network models developed by others in accurately predicting actual operating data from commercial MSF desalination plants. When compared to a data-based neural network, a properly validated model-based process simulation (as in the case of MSF desalination plants) can more effectively quantify the effects of varying operating variables on the desalination performance variables. When it is difficult to develop a model-based process simulation (as in the case of RO desalination plants), we can use a data-based neural network to accurately predict the desalination performance variables.