It is always quite difficult to accurately measure boiler drum water level in power plant. Though the effect of false measurement of water level can be reduced with some devices,the effect of deviation of boiler drum water level to the monitoring and alarm system,even to the control of drum water level,and so on,cannot be surmounted. Because of these reasons,the accurate water level alarm signal cannot be provided and the water level control measures cannot be applied. In order to solve this problem,a water level deviation analysis method is presented for analyzing boiler drum water level. Based on the analysis of boiler drum water level related running parameters,the relational model of water level deviation under different working conditions and its parameters is constructed. By analyzing this model,the specific impacts of the main factors can be fixed. Thus the drum water level deviation can be reduced by adjusting running parameters without changing unit load. And then the measurement of drum water level can be more accurate only if power plants have accurate measuring devices. Therefore,the boiler drum water level can be more accurately monitored and controlled. So,this innovation is important in ensuring the safe running of power plant.
A new car-following model is proposed by considering information from a number of preceding vehicles with intervehicle communication.A supernetwork architecture is first described,which has two layers:a traffic network and a communication network.The two networks interact with and depend on each other.The error dynamic system around the steady state of the model is theoretically analyzed and some nonjam criteria are derived.A simple control signal is added to the model to analyze the criteria of suppressing traffic jams.The corresponding numerical simulations confirm the correctness of the theoretical analysis.Compared with previous studies concerning coupled map models,the controlled model proposed in this paper is more reasonable and also more effective in the sense that it takes into account the formation of traffic congestion.
The rapid expansion of enterprises makes product collaborative design(PCD)a critical issue under the distributed heterogeneous environment,but as the collaborative task of large-scale network becomes more complicated,neither unified task decomposition and allocation methodology nor Agent-based network management platform can satisfy the increasing demands.In this paper,to meet requirements of PCD for distributed product development,a collaborative design mechanism based on the thought of modularity and the Agent technology is presented.First,the top-down 4-tier process model based on task-oriented modular and Agent is constructed for PCD after analyzing the mapping relationships between requirements and functions in the collaborative design.Second,on basis of sub-task decomposition for PCD based on a mixed method,the mathematic model of task-oriented modular based on multi-objective optimization is established to maximize the module cohesion degree and minimize the module coupling degree,while considering the module executable degree as a restriction.The mathematic model is optimized and simulated by the modified PSO,and the decomposed modules are obtained.Finally,the Agent structure model for collaborative design is put forward,and the optimism matching Agents are selected by using similarity algorithm to implement different task-modules by the integrated reasoning and decision-making mechanism with the behavioral model of collaborative design Agents.With the results of experimental studies for automobile collaborative design,the feasibility and efficiency of this methodology of task-oriented modular and Agent-based collaborative design in the distributed heterogeneous environment are verified.On this basis,an integrative automobile collaborative R&D platform is developed.This research provides an effective platform for automobile manufacturing enterprises to achieve PCD,and helps to promote product numeralization collaborative R&D and management development.
