With the introduction of new technologies, such as waste heat recovery units (WHRU), associated gas utilization, the energy flow coupling relationship is further deepened within the energy system of the offshore oil and gas production platform. Besides, the energy system is closely linked with the oil and gas production system, and a closed-loop relationship between energy flow and material flow can be revealed. Uncertainties of energy supply and production process may lead to system-wide fluctuations, which threaten the stable operation of the platform. Therefore, an optimal planning model of integrated energy system for offshore oil and gas production platform is proposed in this paper. Firstly, a generalized energy and material flow model is proposed, three matrixes are defined based on laws of thermodynamics, including energy matrix, process matrix and feedback matrix. Secondly, the energy-material conversion relationship between the energy system and production system of a typical offshore oil and gas platform is quantitatively described, together with the coupling between the input and output of the two systems. Thirdly, considering the energy-material balance constraints and the uncertainties of production system, a multi-objective stochastic planning model for the offshore integrated energy system is established, which takes economics and environmental protection into consideration. A Monte Carlo simulation-based NSGA-II algorithm is proposed to solve the model. Finally, the validity and feasibility of the proposed methodology are demonstrated through an offshore oil and gas platform in Bohai, China. Compared with the traditional planning method, the total cost and CO2 emissions of the proposed method are reduced by 18.9% and 17.3%, respectively.
Offshore micro integrated energy systems are the basis of offshore oil and gas engineering. In order to evaluate its operational risks and ensure the safe development of marine resources, a risk assessment scheme for offshore micro integrated energy systems based on a risk fluid mosaic model is proposed. Aiming at the current situation that the traditional equipment material-energy conversion model has a large amount of modeling and does not fully reflect the system structure, a material-energy conversion model based on unified modeling is constructed, and a risk function is introduced to analyze the material-energy of the power equipment under risk conversion; At the same time, a risk fluid mosaic model based on the system structure and material-energy carrier is constructed to describe the dynamic behavior of risk from the material-energy flow; Aiming at the fact that the traditional risk grading model cannot reflect the overall risk of the system when multiple risks are involved, a multi-weighted system risk grading model is proposed to describe the overall risk situation of the system under multiple risks. The validity and rationality of the model and method proposed in this paper is verified by using an offshore oil and gas platform in the Bohai Sea as a simulation example.INDEX TERMS Offshore micro integrated energy system, unified modeling, risk fluid mosaic model, risk grading, risk assessment.
The Mine Internet of Things (MIoT), as an application paradigm of the IoT in mine environment, realizes the state perception and information interaction by connecting massive smart sensing devices deployed in mine, plays an important role in coal mine production. The fifth-generation (5G) is a key enabling technology to provide an efficient and reliable communication link guarantee for MIoT networks. To meet the increasing demand for huge amounts of data transmission, a NOMA-based heterogeneous MIoT communication system is proposed. In this paper, different types of mine smart devices can occupy the same subchannel resources for data transmission by the NOMA technology, which improves the device access and spectrum utilization of the MIoT system. We aim to maximize the energy efficiency (EE) of all small cell networks via power allocation and subchannel assignment. Under the imperfect channel state information (CSI), a joint power allocation and subchannel assignment iterative algorithm is proposed. Specifically, firstly, by considering the cross-layer interference power constraints, maximum power constraints and QoS constraints, the EE optimization problem is formulated as a mixed integer nonlinear fractional programming problem. Secondly, the uncertain CSI is modeled as an elliptical uncertainty set, and the original problem is transformed into an equivalent convex optimization form by using the Dinkelbach method. Finally, an approximate solution is obtained by using the Lagrangian dual approach. The numerical results validate the effectiveness of the proposed algorithm and significantly show its superior performance as compared with the baseline algorithms.INDEX TERMS Coal mine, 5G, internet of things (IoT), non-orthogonal multiple access (NOMA), heterogeneous network (HetNet), energy efficient, imperfect channel state information
In the actual production of coal mines, the transmission needs of existing underground applications cannot be met due to a lack of strategies and customized equipment for underground 5G application scenarios, which causes increased underground data processing delay and low transmission efficiency. To solve the problem above, the mobile edge computing (MEC) technology based on the 5G wireless base station is studied, and underground 5G communication capabilities is improved by edge caching and dynamic resource allocation according to the actual situation of coal mines. The experimental result shows that under the premise of maintaining the rated power and transmit power of the existing base station, the average delay of executing tasks is 15ms, which is 50% lower than the average delay of all local execution methods. The average delay is reduced by 37.5% than all MEC execution methods. At the same time, the uplink rate of a single base station can reach 1Gbps and the downlink rate can reach 1.5 Gbps. Our method can significantly improve the reliability of mining 5G communication systems and the rational allocation of resources.
Diverse IoT applications, such as unmanned driving, intelligent video, unmanned working face, industrial control, and intelligent robot inspection, are the key technologies in the field of intelligent mines. In order to fully meet the requirements of underground IoT systems of high bandwidth, low latency, and massive connections, it is necessary to study 5G technologies suitable for underground environments to achieve effective deployment in mines. In key areas, such as main transport roadways, fully mechanized mining faces, and underground substations, both spurs and crosstalk in the frequency domain are the dominant factors affecting the stability and reliability of 5G signals. For the purpose of improving the performance of mine 5G, a fusion anti-interference scheme is designed here. Based on a deep complex network and blind source separation, periodic monitoring and filtering suppression of signal interference can be achieved. The test results show that the frequency domain spurs’ suppression capability of the proposed method is 20% higher than that of the traditional equalization method. For frequency domain crosstalk, 90% interference elimination could be achieved by the proposed method without additional delays when compared with the conventional blind source separation. The high-bandwidth and low-latency characteristics of 5G communication can be guaranteed by this method.
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