With the increasing public attention on sustainability, conservation of energy and materials has been a general demand for wastewater treatment plants (WWTPs). To meet the demand, efficient optimal management and decision mechanism are expected to reasonably configure resource of energy and materials.In recent years, advanced computational techniques such as neural networks and genetic algorithm provided data-driven solutions to overcome some industrial problems. They work from the perspective of statistical learning, mining invisible latent rules from massive data. This paper proposes energy and materials-saving management via deep learning for WWTPs, using real-world business data of a wastewater treatment plant located in Chongqing, China. Treatment processes are modeled through neural networks, and materials cost that satisfies single indexes can be estimated on this basis. Then, genetic algorithm is selected as the decision scheme to compute overall cost that is able to simultaneously satisfy all the indexes. Empirically, experimental results evaluate that with the proposed management method, total energy and materials cost can be reduced by 10%-15%.
Initial alignment is one of the key technologies of inertial navigation system, but in moorage, the external disturbance will impair the accuracy in the initial alignment of inertial navigation systems. The purpose of this paper is to design a network parameter of initial alignment which has the capacity of resisting disturbance, analyzing the design method of the alignment loop network parameter, and developing a software to design and verify the network parameter of azimuth fine alignment, automatically. Also, an example is designed to verify the correctness of the software. Computer simulation results indicate that the network parameter is designed and verified by the software automatically according to the requirement of the parameter.
Index Terms -Inertial navigation system; azimuth fine alignment; software design; parameter design
In order to improve the accuracy of strapdown inertial navigation system, this paper analyze the system error cause by gyro drift with different characteristic deeply, and brought the single axis to-and-fro rotation monitor technique to modulate the inertial sensor error. In the rotation modulation scheme, the error sources which dominate the modulation effect is brought, that is the constant gyro drift, the gyro scale error and the deviation of the gyro installation error. The monitor scheme based on single axis to-and-fro is confirmed by the modulation effect towards the former three error compared with the single axis continuous rotation scheme.Computer simulation show that the scheme can modulate the inertial sensor error effectively and improve the position accuracy of SINS greatly.
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