Issues related to tyre safety are one of the major concerns when discussing the motor-vehicles' safety. Moreover, various articles reported in the print and electronic media on tyre failures, particularly involving retread tyres were often associated as the cause of crashes. However, before any effective solutions can be proposed to the relevant authorities, weaknesses and loopholes in the current ecosystem needs to be identified. Thus, this paper is aimed at reviewing the current status of motor vehicle tyre ecosystem in the country and establishing the knowledge on current related issues. This paper will look into five stages in the ecosystem, from tyre production until disposal and tyre scraping. Current and potential gaps are identified and recommendations to improve the situations are put forward. To enable a clearer explanation of the issues found and the proposed recommendations, the findings are tabulated according to each stage. The findings are expected to provide useful insights into the current status and issues in the ecosystem, and providing a useful and beneficial method of solution.
Aiming at the problems of the current production and operation status of the progressive cavity pump (PCP) in coalbed methane (CBM) wells which cannot be timely monitored, quantitatively evaluated, and accurately predicted, a five-step method for evaluating and predicting the health status of PCP wells is proposed: data preprocessing, principal parameter optimization, health index construction, health degree division, and health index prediction. Therein, a health index (HI) formulation was made based on deep learning, and a statistical method was used to define the health status of PCP wells as being healthy, subhealthy, or faulty. This allowed further research on the HI prediction model of PCP wells based on the long short-term memory (LSTM) network. As demonstrated in the study, they can reflect both the change trend and the contextual relevance of the health status of PCP wells with high accuracy to achieve real-time, quantitative, and accurate assessment and prediction. At the same time, the conclusion gives good guidance on the production performance analysis and failure warning of the PCP wells and suggests a new direction for the health status assessment and warning of other artificial lift equipment.
In view of the problems that there are many well control risk points and the situation is grim of Xinjiang No.1 gas production plant, this paper carries out the gas well integrity evaluation and risk assessment, and establishes the comprehensive fuzzy evaluation model (FCEM) of gas well integrity. This paper analysed the integrity status of gas wells in Xinjiang No.1 gas production plant, establishes the integrity evaluation index system with well barrier components, real-time dynamic index and management organization as the main influence factors, determines the membership function of each index, calculates the weight of each index by using analytic hierarchy process(AHP), and establishes the risk degree calculation model by using fuzzy comprehensive evaluation, Quantitative analysis of gas well integrity. In this paper, a case study of a well in Xinjiang No.1 gas production plant shows that the model can quantitatively calculate the risk of gas well integrity and provide a reference for early warning of gas well integrity failure.
The system efficiency prediction of pumping unit well always plays an important part in dynamic analysis of oil field production. The system efficiency of pumping unit well is restricted by a variety of factors, which is extremely complex. Ideal effect cannot be realized when applying structural causal models to forecast it. The chaotic time series forecasting model of system efficiency for pumping unit well is established in this paper; CC method is used to determine the embedding dimension of phase space reconstruction parameters and delay time; the maximum Lyapunov exponent, which is figured out through small-data method, is used to detect the chaos of time series; the system efficiency of pumping unit well is predicted through univariate and multivariate time series forecasting method. The experiment verifies that chaotic time series forecasting method can be used in accurate system efficiency forecasting.
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