Gas drainage is carried out based on output from each coal bed throughout commingling production of coalbed methane (CBM). A reasonable drainage process should therefore initially guarantee main coal bed production and then enhance gas output from other beds. Permanent damage can result if this is not the case, especially with regard to fracture development in the main gas-producing coal bed and can greatly reduce single well output. Current theoretical models and measuring devices are inapplicable to commingled CBM drainage, however, and so large errors in predictive models cannot always be avoided. The most effective currently available method involves directly measuring gas output from each coal bed as well as determining the dominant gas-producing unit. A dynamic evaluation technique for gas output from each coal bed during commingling CBM production is therefore proposed in this study. This technique comprises a downhole measurement system combined with a theoretical calculation model. Gas output parameters (i.e., gas-phase flow rate, temperature, pressure) are measured in this approach via a downhole measurement system; substituting these parameters into a deduced theoretical calculation model then means that gas output from each seam can be calculated to determine the main gas-producing unit. Trends in gas output from a single well or each seam can therefore be predicted. The laboratory and field test results presented here demonstrate that calculation errors in CBM outputs can be controlled within a margin of 15% and therefore conform with field use requirements.

In order to improve the performance of surface plasmon resonance (SPR) biosensor, the structure based on two-dimensional (2D) of graphene and transition metal dichalcogenides (TMDCs) are proposed to greatly enhance the Goos-Hänchen (GH) shift. It is theoretically proved that GH shift can be significantly enhanced in SPR structure coated with gold (Au)-indium tin oxide (ITO)-TMDCs-graphene heterostructure. In order to realize high GH shifts, the number of TMDCs and graphene layer are optimized. The highest GH shift (−801.7 λ) is obtained by Au-ITO-MoSe2-graphene hybrid structure with MoSe2 monolayer and graphene bilayer, respectively. By analyzing the GH variation, the index sensitivity of such configuration can reach as high as 8.02 × 105 λ/RIU, which is 293.24 times of the Au-ITO structure and 177.43 times of the Au-ITO-graphene structure. The proposed SPR biosensor can be widely used in the precision metrology and optical sensing.

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The surface plasmon resonance (SPR) sensor offers high sensitivity, good stability, simple structure, and is label-free. However, optimizing a multi-layered structure is quite time-consuming within the SPR sensor design process. Moreover, it is easy to overlook optimal design when using the conventional parameter sweeping method. In this paper, the improved particle swarm optimization (IPSO) algorithm with high global optimal solution convergence speed is applied for this purpose. Based on the IPSO algorithm, the SPR sensor with transition metal dichalcogenides (TMDCs) and graphene composite is proposed and optimized. The results show that the best Ag-ITO- W S 2 -graphene hybrid structure can be found by the IPSO algorithm, and the maximum sensitivity is 137.4°/RIU, and the figure of merit (FOM) is 5.25 R I U − 1 . Compared with the standard particle swarm optimization algorithm, the number of iterations can be reduced. The development of the SPR sensor provides an optimization platform, which enormously improves the development efficiency of the multi-layer SPR sensor.