Aipeng Jiang

Mathematical Modeling and Simulation of SWRO Process Based on Simultaneous Method

Reverse osmosis (RO) technique is one of the most efficient ways for seawater desalination to solve the shortage of freshwater. For prediction and analysis of the performance of seawater reverse osmosis (SWRO) process, an accurate and detailed model based on the solution-diffusion and mass transfer theory is established. Since the accurate formulation of the model includes many differential equations and strong nonlinear equations (differential and algebraic equations, DAEs), to solve the problem efficiently, the simultaneous method through orthogonal collocation on finite elements and large scale solver were used to obtain the solutions. The model was fully discretized into NLP (nonlinear programming) with large scale variables and equations, and then the NLP was solved by large scale solver of IPOPT. Validation of the formulated model and solution method is verified by case study on a SWRO plant. Then simulation and analysis are carried out to demonstrate the performance of reverse osmosis process; operational conditions such as feed pressure and feed flow rate as well as feed temperature are also analyzed. This work is of significant meaning for the detailed understanding of RO process and future energy saving through operational optimization.

Offset-free MPC with zone control for an air source floor heating system

In this study, an offset-free model predictive control (MPC) with zone control is proposed for an air source heat pump connected to a residential floor heating system. First, both the building model and the heat pump model are developed. A model transformation strategy is presented to transform the system nonlinearity into linear system with input uncertainties. Then an offset-free MPC is adopted to handle system uncertainties. Zone control target is achieved based on the proposed offset-free MPC to keep the room temperature within a specified range. This strategy enables the control system more freedom to reduce the energy consumption. Simulation results demonstrate that the proposed approach can provide an improved air conditioning performance and energy efficiency over conventional PI controllers.

Operational Optimization of Large-Scale Parallel-Unit SWRO Desalination Plant Using Differential Evolution Algorithm

A large-scale parallel-unit seawater reverse osmosis desalination plant contains many reverse osmosis (RO) units. If the operating conditions change, these RO units will not work at the optimal design points which are computed before the plant is built. The operational optimization problem (OOP) of the plant is to find out a scheduling of operation to minimize the total running cost when the change happens. In this paper, the OOP is modelled as a mixed-integer nonlinear programming problem. A two-stage differential evolution algorithm is proposed to solve this OOP. Experimental results show that the proposed method is satisfactory in solution quality.

An Improved Finite Element Meshing Strategy for Dynamic Optimization Problems

The finite element orthogonal collocation method is widely used in the discretization of differential algebraic equations (DAEs), while the discrete strategy significantly affects the accuracy and efficiency of the results. In this work, a finite element meshing method with error estimation on noncollocation point is proposed and several cases were studied. Firstly, the simultaneous strategy based on the finite element is used to transform the differential and algebraic optimization problems (DAOPs) into large scale nonlinear programming problems. Then, the state variables of the reaction process are obtained by simulating with fixed control variables. The noncollocation points are introduced to compute the error estimates of the state variables at noncollocation points. Finally, in order to improve the computational accuracy with less finite element, moving finite element strategy was used for dynamically adjusting the length of finite element appropriately to satisfy the set margin of error. The proposed strategy is applied to two classical control problems and a large scale reverse osmosis seawater desalination process. Computing result shows that the proposed strategy can effectively reduce the computing effort with satisfied accuracy for dynamic optimization problems.

A Rescheduling Approach for a Large-scale Reverse Osmosis Desalination Plant under Uncertain Fresh Water Demand

Abstract The scheduling problem of a large-scale seawater reverse osmosis (SWRO) desalination plant is considered as a mixed-integer nonlinear programming problem (MINLP) over the time horizon. It is hard to find out the exact solution directly, especially when the working environment is changing. So, when the variables are changed, it is needed to find out a new scheduling plan quickly to meet the changes. In this paper, a rescheduling model of operational optimization of a large-scale SWRO is built, which is a bi-objective optimization with criterions of efficiency and stability. A rolling horizon approach (RHA) is used to make the solution meet the requirements of accuracy and rapidity. The framework of RHS is structured and a two-stage differential evolution (TSDE) algorithm is used to compute the solution. A simulation based on a real case with fluctuations in freshwater demand in Liuheng Seawater Desalination Plant in Zhejiang province was made, and the results show that the performance of the proposed algorithm is satisfied.

A Dynamic Optimization Strategy for the Operation of Large Scale Seawater Reverses Osmosis System

In this work, an efficient strategy was proposed for efficient solution of the dynamic model of SWRO system. Since the dynamic model is formulated by a set of differential-algebraic equations, simultaneous strategies based on collocations on finite element were used to transform the DAOP into large scale nonlinear programming problem named Opt2. Then, simulation of RO process and storage tanks was carried element by element and step by step with fixed control variables. All the obtained values of these variables then were used as the initial value for the optimal solution of SWRO system. Finally, in order to accelerate the computing efficiency and at the same time to keep enough accuracy for the solution of Opt2, a simple but efficient finite element refinement rule was used to reduce the scale of Opt2. The proposed strategy was applied to a large scale SWRO system with 8 RO plants and 4 storage tanks as case study. Computing result shows that the proposed strategy is quite effective for optimal operation of the large scale SWRO system; the optimal problem can be successfully solved within decades of iterations and several minutes when load and other operating parameters fluctuate.

Research of PSO algorithm with variable constraints in process system

To solve chemical problems with variable and nonrigid constraints, a method based on particle swarm optimization (PSO) algorithm was presented. By mathematical analysis and transform, the variable constraints were regard as an item to be optimized. Then the item multiplied by penalty and combined with the primary objective function. So the primary problem was transferred to the multi-objective function, and can be solved by multi-objective PSO algorithm. With problems solved by multi-objective PSO and analysis of the solutions related with variable constraints, reasonable solution and optimal scheme can be obtained. The proposed method was used to optimize a chemical design problem and a parameter estimation problem. The results demonstrate that the proposed method is effective.

Research on control method combined with load coordinate for dry desulfurization of slurry fluidized bed boiler

It is the most effective resource use practices for slurry and slime to be used as fuel for Fluidized bed boiler. The dry desulfurization of sludge Fluidized bed boiler is a large time delay system, and load disturbance of this system changes frequently. In order to achieve stable control of SO2 emission, and meet environmental requirements, a fuzzy control technology combined with the optimal feed-forward was designed. Combined with field experience, fuzzy controller was designed by fuzzy control technology, and then the integral process was added to achieve non-error track. Based on the objective to minimize disturbance impact, and in order to coordinate the desulfurization control and steam load control, a nonlinear programming problem for solving the optimal feed-forward parameters was established, from which the most excellent feed-forward form can be obtained. Results of 440T/H fluidized bed boiler show that the proposed method has satisfactory control effect. SO2concentration can fully meet environmental emissions requirements, and its fluctuation is relatively small.

Performance analysis of algorithms for large-scale nonlinear optimization

With the development of optimization heads toward large-scale problems, a series of optimization packages were developed for large-scale NLP optimization. The RSQP (reduced sequential quadratic programming) we concerned is one of best large scale NLP algorithm, and is especially efficient in process operation optimization. In this paper, the performance of our concerned algorithm RSQP, CONOPT2 and MINOS5 were tested by large-scale benchmarking COPS examples, the calculation was under the environment of GAMS. Computing results demonstrate that the performance of CONOPT2 was better than the others on robustness and efficiency. To problems with relatively small degrees of freedom, the performance of MINOS5 worked worst, though RSQP was not as robust as CONOPT2, its memory required reduced as half as CONOPT2. The results show that the RSQP we concerned has great advances for large scale optimization and further research for the algorithmpsilas stability is very necessary.

Optimal design of intake system mufflers in engine based on single and multi-objective optimization

Recently, more attention was put on the noise control of automotive. To reduce the noise of intake system of some kind of automotive, a model of automotive intake system was found based on transfer matrix method and black box experimental method. Then to fulfill the requirement of design and realize better anechoic effect, single objective optimization and multi-objective problem was formulated. To get the optimal performance and the optimal structure parameters, the problems were solved by genetic algorithm (GA) and NSGAII through Matlab platform, and optimal analysis was carried out to get the optimal parameters under different requirements and soft constraints. The results of our work have the guiding sense to the reality production.