Bypass Optimization Operating And Structure Controllability Analysis Of Heat Exchanger Networks

With the rapid development of economy, global energy consumption is increasing, and there is a serious shortage of energy. As one of the main tools of industrial process energy recovery system, heat exchanger network improves the efficiency of energy recovery, rationally and effectively saving energy is important. Over the years, studies of heat exchanger network optimization problems has made some achievements, but most of the heat exchanger network optimization method considers only the economy, ignoring the operability, controllability and other network performance, so the optimization design for the bypass provides a new research ideas.Heat exchanger network synthesis method in general does not set enough bypass, utilities only regulate target temperature, and thus can not meet the requirements of the degree of freedom, less control for network. So this paper proposes set bypass in the network, the opening of the bypass means of regulation, minimal operating costs means of the objective function, using particle swarm algorithm model, and finally determined the number and position of the bypass.three examples demonstrate the effectiveness of this method.The actual engineering implementation, it is required to meet the target temperature of the heat exchanger network, in order to make the network running smoothly and save costs, we must to reduce the interference as more as possible. In this paper, purpose to analyze the control capability of the network, then set some of types of interference, and tuning variables, the operating cost is calculated as the objective function for solving. Finally, three examples illustrate the adjustment of bypass opening can control the network and save the energy.Basing on the heat exchanger network structure and control theory, combined with graph theory methods and correlation matrix to calculate the structural controllability matrix of the network, to determine whether the network can meet the control requirements, and by analysis redundancies under different control scheme chooses the optimal control scheme. Finally, three examples to verify the feasibility of the method.