Steady State and Dynamic Modeling of Spiral Wound Wastewater Reverse Osmosis Process

yesReverse osmosis (RO)is one of the most important technologies used in wastewater treatment plants due to high contaminant rejection and low utilization of energy in comparison to other treatment procedures. For single-component spiral-wound reverse osmosis membrane process, one dimensional steady state and dynamic mathematical models have been developed based on the solution-diffusion model coupled with the concentration polarization mechanism. The model has been validated against reported data for wastewater treatment from literature at steady state conditions. Detailed simulation using the dynamic model has been carried out in order to gain deeper insight of the process. The effect of feed flow rate, pressure, temperature and concentration of pollutants on the performance of the process measured in terms of salt rejection, recovery ratio and permeate flux has been investigated.The full text will be available at the end of the publisher's embarg

Wastewater Treatment by Spiral Wound Reverse Osmosis: Development and Validation of a Two Dimensional Process Model

yesReverse osmosis (RO) has become a significant method for removing salts and organic compounds from seawater and wastewater in recent decades. Spiral-wound module has been widely used due to a number of special features such as high packing density, premium separation and low operating cost. In this paper, a two-dimensional mathematical model is developed for the transport of dilute aqueous solutions through a spiral-wound RO module and the operational characteristics of the process under steady state conditions are analysed. The model is based on the solution-diffusion model coupled with the concentration polarization mechanism. This model yields a set of Differential and Algebraic Equations (DAEs), which are solved using the gPROMS software. The model is validated using experimental data from the literature for the rejection of dimethylphenol as solute in aqueous solutions. The model is then used to simulate the process under steady state conditions to gain deeper insight of the process

Modeling of a spiral-wound reverse osmosis process and parameter estimation

YesReverse osmosis system has been widely used for the separation of organic and non-organic pollutants present in wastewater. The main aim of this study is to develop a one dimensional steady state model based on the three-parameter Spiegler-Kedem methodology using the gPROMS software and validate it by assessing the performance of membrane rejection for the separation data of aqueous solutions of phenol under different concentrations and pressures. Considerations of the variance of pressure, flow rate, solute concentration, solvent and solute fluxes and mass transfer coefficient along the feed channel were included in the model. Furthermore, an optimization methodology for the gEST parameter estimation tool has been developed in the gPROMS and used with experimental data in order to estimate the best values of the separation membrane parameters and the friction parameter. The simulation results of this model have been corroborated by experimental data

Optimal reverse osmosis network configuration for the rejection of dimethylphenol from wastewater

YesReverse osmosis (RO) has long been recognised as an efficient separation method for treating and removing harmful pollutants, such as dimethylphenol in wastewater treatment. This research aims to study the effects of RO network configuration of three modules of a wastewater treatment system using a spiral-wound RO membrane for the removal of dimethylphenol from its aqueous solution at different feed concentrations. The methodologies used for this research are based on simulation and optimisation studies carried out using a new simplified model. This takes into account the solution-diffusion model and film theory to express the transport phenomena of both solvent and solute through the membrane and estimate the concentration polarization impact respectively. This model is validated by direct comparison with experimental data derived from the literature and which includes dimethylphenol rejection method performed on a small-scale commercial single spiral-wound RO membrane system at different operating conditions. The new model is finally implemented to identify the optimal module configuration and operating conditions that achieve higher rejection after testing the impact of RO configuration. The optimisation model has been formulated to maximize the rejection parameters under optimal operating conditions of inlet feed flow rate, pressure and temperature for a given set of inlet feed concentration. Also, the optimisation model has been subjected to a number of upper and lower limits of decision variables, which include the inlet pressure, flow rate and temperature. In addition, the model takes into account the pressure loss constraint along the membrane length commensurate with the manufacturer’s specifications. The research clearly shows that the parallel configuration yields optimal dimethylphenol rejection with lower pressure loss

Simulation and sensitivity analysis of spiral wound reverse osmosis process for the removal of dimethylphenol from wastewater using 2-D dynamic model

YesReverse Osmosis (RO) processes are readily used for removing pollutants, such as dimethylphenol from wastewater. A number of operating parameters must be controlled within the process constraints to achieve an efficient removal of such pollutants. Understanding the process dynamics is absolutely essential and is a pre-step for designing any effective controllers for any process. In this work, a detailed distributed two-dimensional dynamic (x and y dimensions and time) model for a spiral-wound RO process is developed extending the 2-D steady state model of the authors published earlier. The model is used to capture the dynamics of the RO process for the removal of dimethylphenol from wastewater. The performance of the 2-D model is compared with that obtained using 1-D dynamic model before the model is being used to investigate the performance of the RO process for a range of operating conditions

Modelling, Simulation, and Optimisation of Reverse Osmosis Process with Application in Wastewater Treatment and Food Processing

Reverse Osmosis (RO) is a membrane-based separation process applied in several industrial and food processing applications. In this research, performance of RO process is investigated in respect of two applications (a) wastewater treatment (b) concentration fruit juices using model-based techniques. For this purpose, a number of models (both 1 and 2-dimensional steady state and dynamic) for spiral wound RO process are developed based on Solution-Diffusion model and Irreversible Thermodynamic model. The models are validated against actual experimental data reported in the literature before being used in further simulation and optimisation studies for both wastewater treatment and fruit juice concentration. Wastewater effluents of many industrial applications contain a variety of micro-pollutants and highly-toxic compounds, which are released into a variety of water resources. Such pollutants not only disrupt the biological ecosystem, but they also pose a real threat to the water supply for human consumption and to the aquatic ecosystems. The earlier chapters of the thesis evaluate the performance of RO process in terms of removal efficiency of toxic compounds such as chlorophenol, N-nitrosamine, etc. from wastewater. The effect of several operating parameters such as feed pressure, concentration, flow rate and temperature, on the performance of RO process are evaluated. Also, suitability of a number of different RO configurations for efficient removal of toxic compounds are evaluated. For example, (a) two-stage/two-pass RO design synthesis of RO network for the removal of chlorophenol (b) multistage multi-pass RO process with and without energy recovery option for the removal of N-nitrosamine are investigated. The dynamic response of the RO process for step changes in the operating parameters is investigated for the removal of phenolic compounds. Finally, in the context of wastewater treatment, a case study with multi compounds contaminants is suggested where a multi-objective optimisation problem has achieved the optimum rejection of all the compounds and recovery rate. In respect of food processing, RO has been considered as a prominent process in fruit juice concentration due to its ability to effectively retain the flavour, sensory, aroma and nutritional characteristics and concentrate the juice. This research elucidates one example of apple juice concentration process and focuses on highlighting successful modelling and optimisation methodology. This in turn provides an efficient method of RO process for concentrating apple juice by improving the reliability and efficiency of the underlying separation and concentration process.Ministry of High Education and Scientific Research of Ira

Economic removal of chlorophenol from wastewater using multi-stage spiral-wound reverse osmosis process: simulation and optimisation

YesThe successful use of Reverse Osmosis (RO) process has increased significantly in water desalination, water treatment and food processing applications. In this work, the economic feasibility of a multi-stage RO process including both retentate and permeate reprocessing for the removal of chlorophenol from wastewater is explored using simulation and optimisation studies. Firstly, a mathematical model of the process is developed based on the solution diffusion model, which was validated using experimental chlorophenol removal from the literature, is combined with several appropriate cost functions to form a full model package. Secondly, for a better understanding of the interactions between the different parameters on the economic performance of the process, a detailed process simulation is carried out. Finally, a multi-objective optimisation framework based on Non-Linear Programming (NLP) problem is developed for minimising the product unit cost, the total annualised cost, the specific energy consumption together with optimising the feed pressure and feed flow rate for an acceptable level of chlorophenol rejection and total water recovery rate. The results clearly show that the removal of chlorophenol can reach 98.8% at a cost of approximately 0.21 $/m³

Dynamical Analysis and Robust Control Synthesis for Water Treatment Processes

Nowadays, water demand and water scarcity are very urgent issues due to population growth, drought and poor water quality all over the world. Therefore, water treatment plants are playing a vital role for good living condition of human. Water area needs more concentration study to increase water productivity and decrease water cost. This dissertation presents the analysis and control of water treatment plants using robust control techniques. The applied control algorithms include H∞, gain scheduled and observer-based loop-shaping control technique. They are modern control algorithms and very powerful in robust controlling of systems with uncertainties and disturbances. The water treatment plants include a desalination system and a wastewater process. Since fresh water scarcity is getting more serious, the desalination plants are to produce drinking water and wastewater treatment plants give the reusable water. The desalination system is a RO one used to produce drinking water from seawater and brackish water. The nonlinear behaviors of this system is carefully analyzed before the linearization. Due to the uncertainty caused by concentration polarization, the system is linearized using linear state-space parametric uncertainty framework. The system also suffer from many disturbances which water hammer is one of the most influential one. The mixed robust H∞ and μ-synthesis control algorithm is applied to control the RO system coping with large uncertainties, disturbances and noises. The wastewater treatment process is an activated sludge process. This biological process use microorganisms to convert organic and certain inorganic matter from wastewater into cell mass. The process is very complex with many coupled biological and chemical reactions. Due to the large variation in the influent flow, the system is modelized and linearized as a linear parametric varying system using affine parameter-dependent representation. Since the influent flow is quickly variable and easily to be measured, the robust gain scheduled robust controller is applied to deal with the large uncertainty caused by the scheduled parameter. This control algorithm often gives better performances than those of general robust H∞ one. In the wastewater treatment plant, there exist an anaerobic digestion, which is controlled by the observer-based loop-shaping algorithm. The simulations show that all the controllers can effectively deal with large uncertainties, disturbances and noises in water treatment plants. They help improve the system performances and safeties, save energy and reduce product water costs. The studies contribute some potential control approaches for water treatment plants, which is currently a very active research area in the world.Contents ······················································································· iv List of Tables ··············································································· viii List of Figures ··············································································· ix Chapter 1. Introduction ···································································· 1 1.1 Reverse osmosis process ···································································· 2 1.2 Activated sludge process ···································································· 6 1.3 Robust H∞ and gain scheduling control ··················································· 10 Chapter 2. Robust H∞ controller ······················································· 13 2.1 Introduction ·················································································· 13 2.2 Uncertainty modelling ······································································ 13 2.2.1 Unstructured uncertainties ···························································· 14 2.2.2 Parametric uncertainties ······························································· 15 2.2.3 Structured uncertainties ································································ 16 2.2.4 Linear fractional transformation ······················································ 16 2.3 Stability criterion ············································································ 17 2.3.1 Small gain theorem ····································································· 17 2.3.2 Structured singular value (muy) synthesis brief definition ·························· 19 2.4 Robustness analysis and controller design ··············································· 20 2.4.1 Forming generalised plant and N-delta structure ····································· 20 2.4.2 Robustness analysis ···································································· 24 2.5 Reduced controller ·········································································· 26 2.5.1 Truncation ··············································································· 27 2.5.2 Residualization ········································································· 29 2.5.3 Balanced realization···································································· 29 2.5.4 Optimal Hankel norm approximation ················································ 31 Chapter 3. Robust gain scheduling controller ······································· 37 3.1 Introduction ·················································································· 37 3.2 Linear parameter varying (LPV) system ·················································· 39 3.3 Matrix Polytope ·············································································· 40 3.4 Polytope and affine parameter-dependent representation ······························· 41 3.4.1 Polytope representation ································································ 41 3.4.2 Affine parameter-dependent representation ········································· 42 3.5 Quadratic stability of LPV systems and quadratic (robust) H∞ performance ········· 43 3.6 Robust gain scheduling ····································································· 44 3.6.1 LPV system linearization ······························································ 44 3.6.2 Polytope-based gain scheduling ······················································ 45 3.6.3 LFT-based gain scheduling ··························································· 48 Chapter 4. Mixed robust H∞ and μ-synthesis controller applied for a reverse osmosis desalination system ····························································· 52 4.1 RO principles ················································································ 52 4.1.1 Osmosis and reverse osmosis ························································· 52 4.1.2 Dead-end filtration and cross-flow filtration ········································ 53 4.2 Membranes ··················································································· 54 4.2.1 Structure and material ································································· 54 4.2.2 Hollow fine fiber membrane module ················································ 55 4.2.3 Spiral wound membrane module ····················································· 57 4.3 Nonlinear RO modelling and analysis ···················································· 58 4.3.1 RO system introduction ······························································· 58 4.3.2 Modelling ··············································································· 60 4.3.3 Nonlinear analysis ······································································ 62 4.3.4 Concentration polarization ···························································· 64 4.4 Water hammer phenomenon ······························································· 66 4.4.1 Water hammer, column separation and vaporous cavitation ······················ 66 4.4.2 Water hammer analysis and simulation ·············································· 69 4.4.3 Prevention of water hammer effect··················································· 78 4.5 RO linearization ············································································· 79 4.5.1 Nominal linearization ·································································· 79 4.5.2 Uncertainty modeling ·································································· 81 4.5.3 Parametric uncertainty linearization ················································· 83 4.6 Robust H∞ controller design for RO system ·············································· 85 4.6.1 Control of uncertain RO system ······················································ 85 4.6.2 Robustness analysis and H∞ controller design ······································ 86 4.7 Simulation result and discussion··························································· 90 4.8 Conclusion ··················································································· 95 Chapter 5. Robust gain scheduling control of activated sludge process ······· 96 5.1 Introduction about activated sludge process ············································· 96 5.1.1 State variables ·········································································· 98 5.1.2 ASM1 processes ······································································ 100 5.1.3 The control problem of activated sludge process ································· 102 5.2 System modelling ········································································· 104 5.3 Model linearization ········································································ 107 5.4 Robust gain-schedule controller design for activated sludge process ··············· 109 5.5 Simulation result and discussion························································· 115 5.6 Conclusion ················································································· 120 Chapter 6. Observer-based loop-shaping control of anaerobic digestion ···· 121 6.1 Introduction ················································································ 121 6.1.1 Control problem in anaerobic digestion ··········································· 122 6.2 System modelling ········································································· 123 6.3 Controller design ·········································································· 124 6.3.1 H∞ loop-shaping controller ························································· 125 6.3.2 Coprime factor uncertainty ·························································· 126 6.3.3 Control synthesis ····································································· 127 6.4 Simulation result ··········································································· 131 6.5 Conclusion ················································································· 133 Chapter 7. Conclusion ··································································· 134 References ·················································································· 136 Appendices ················································································· 144Docto

Process Modeling of Forward Osmosis and Pressure Retarded Osmosis Integration with Seawater Reverse Osmosis

Osmotically driven membrane processes, like forward osmosis and pressure retarded osmosis, may hold key advantages when integrated with reverse osmosis for seawater desalination. The spiral-wound membrane platform in which these processes are applied has inherent disadvantages that need to be explored. Maintaining proper operating pressure in both of the fluid channels of a spiral-wound membrane requires the feed and draw streams to be operated at different flow rates, often as drastic as a 1:10 ratio. This affects the thermodynamic equilibrium of the system and drastically affects potential water and energy recovery. In this work, a model was created to rigorously represent spiral-wound membranes to increase modeling accuracy. A process configuration that features periodic recharging of the stream inside of the envelope is proposed to mitigate the effects of the flow rate difference. The model is used to compare the multi-stage design to single-stage configurations for both forward osmosis and pressure retarded osmosis by testing various feed and draw flow rate ratios, between 1:10 to 1:1, operated by each process as well as important membrane characteristics such as channel height and water and salt permeability. The multi-stage design shows an increase in wastewater utilization from 62.6% to 90% when compared to the single-stage designs for forward osmosis. Additionally, the multi-stage configuration increases the pressure retarded osmosis specific energy recovery from 0.13 kWh/m3 to 0.55 kWh/m3. However, the increased effectiveness of these multi-staged designs comes with a reduction in average water flux and power density, which leads to the requirement of more membrane area and capital investment for potential system implementation

Simulation of reverse osmosis process: Novel approaches and development trends

Reverse osmosis is an essential technological separation process that has a large number of practical applications. The mathematical simulation is significant for designing and determining the most effective modes of membrane equipment operation and for a deep understanding of the processes in membrane units. This paper is an attempt at systematization and generalizing the results of the investigations dedicated to reverse osmosis simulation, which was published from 2011 to 2020. The main approaches to simulation were analyzed, and the scope of use of each of them was delineated. It was defined that computational fluid dynamics was the most used technique for reverse osmosis simulation; the intensive increase in using of molecular dynamics methods was pointed out. Since these two approaches provide the deepest insight into processes, it is likely that they will further be widely used for reverse osmosis simulations. At the same time, for the simulation of the membrane plant, it is reasonable to use the models that required the simplest solutions methods. The solution-diffusion model appears to be the most effective and flexible for these purposes. Therefore, this model was widely used in considering the period. The practical problems solved using each of the considered approaches were reviewed. Moreover, the software used for the solution of the mathematical models was regarded