With the increasing demands for higher treatment efficiency, better effluent quality, and energy conservation in Urban Wastewater Treatment Plants (WWTPs), research has already been conducted to construct an optimized control system for Anaerobic-Anoxic-Oxic (AAO) process using a data-driven approach. However, existing data-driven optimization control systems for AAO mainly focus on improving effluent water quality and reducing energy consumption, therefore they lack consideration for the stability of bioreactors. Meanwhile, safety in the optimization control process is still missing, resulting in a lack of reliability in practical applications. In this study, long short-term memory based model-predictive control (LSTM-MPC) with safety verificationis developed for the real-time control of AAO. It is used to optimize the control of aeration volume, internal recirculation, and sludge internal recycle processes for both saving energy and maintaining the stability of the bioreactor operation. To ensure the safety of the control process, this study proposes three rationality verification methods based on historical operation experience. These methods are validated through data from a real-world WWTP in eastern China. The results show that the prediction model of LSTM-MPC is capable of accurately predicting the water quality variables of the AAO system, with mean square error (MSE) close to 2.64 and Nash–Sutcliffe model efficiency coefficient (NSE) of 0.99 on the validation dataset. The combination of LSTM-MPC and rationality verification achieves a stable control trajectory with a 7% reduction in oxygen usage compared to a conventional controller, demonstrating its efficacy as a safe and reliable control strategy for WWTPs.
The purpose of this research is to develop a China-adapted AEZ methodology and applies it to assesse food productivity potential of cultivated land in China. The methodology of AEZ (Agro-Ecological Zones) is explored. Under the land utilization types and agricultural inputs of year 2000, the food productivity potential of cultivated land is assessed as follows: cereal 589.8 million ton, oil-bearing crops 30.17 million ton, and sugar crops 97.4 million ton. Results show that AEZ is an effective method for assessing land productivity potential in macro level. However, when applied to regional scale, further validation of results is needed due to inadequate accuracy of data and calibration issues.
Summary The lithologic trap related to sublacustrine fan has become a hot field in Bohai Oilfield, while genetic types and subtly characterization of sand bodies are still unclear in Liaozhong Sag of Paleogene. Braided river delta deposits on the slope can be transported into the lake floor and form the sublacustrine fan under the trigger of slump, which can be divided into the inner fan subfacies dominated by slides-slump, the middle fan subfacies dominated by debris flow and the outer fan subfacies dominated by turbidity current. Different subfacies have obvious seismic response, respectively characterized by strong parallel reflection, imbricated reflection and weak discontinueous reflection. Reservoir inversion and attribute analysis are used to track the envelope of reservoirs. Under the constraints of seismic attributes and formation thickness, these responses subtly depict the favorable reservoirs. Due to the bulk freezing characteristics of debris flow, sand-rich bodies can deposit on the slope of lake floor. The superposition of multiple gravity flow events can form continuous middle fans and advance to the central basin, which has great significance to expand the exploration in the field of deep water.
The accurate simulation of the dynamics of the anaerobic–anoxic–oxic (A2O) process in the biochemical reactions in wastewater treatment plants (WWTPs) is important for system prediction and optimization. Previous studies have used real-time monitoring data of WWTPs to develop data-driven predictive models, but these models cannot be used to provide mathematical analysis of A2O dynamic properties. In this study, we developed a new simulation and analysis method for determining A2O dynamics in biochemical reactions using deep learning and the Koopman operator to address the above problems. This method was validated through data from a real-world WWTP in east China and compared it with the traditional deep learning model. According to the results, the new method achieved high-accuracy prediction. Meanwhile, with the help of the Koopman operator, the new method was able to analyze the asymptotical stability and convergence behavior of the A2O process, which provides a brand-new perspective for the in-depth study of biochemical reactor dynamics.
Chengbei area, located in the western Bohai Sea, has great exploration potential, but it is difficult to implement conventional seismic processing technology because of the lack of logging information and low resolution of seismic data. In view of this feature, this paper adopts the seismic high-resolution processing technology based on compressed sensing. This method uses the principle of compressed sensing to recover the full band seismic reflection information to compensate the high and low-frequency parts of attenuation, and can be implemented in the areas lacking logging information. The processing results show that the method can effectively broaden the seismic frequency band, identify thin reservoirs and improve the resolution of seismic data. The results of treatment are highly consistent with the drilled wells, and the results in the no-well area are in line with the understanding of geological deposition and have high reliability. Note: This paper was accepted into the Technical Program but was not presented at IMAGE 2021 in Denver, Colorado.
For the case of transmission characteristic in shallow water channel, broadband acoustic model have to be considered. A rapid numerical prediction theory of underwater broadband signal waveform is studied in this paper. During building broadband acoustic model in shallow water with a thermocline, based on beam-displacement ray-mode theory (BDRM), approximate expansion of broadband acoustic model with respect to frequency and model parallelization will be used for rapid and accurate broadband signal waveform prediction. According to compare result, it offers a satisfactory degree of accuracy and the calculating speed has been improved comparing with conventional mode method.
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