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Direct methanol fuel cells are one of the most promisingly critical fuel cell technologies for portable applications. Due to the strong dependency between actual operating conditions and electrical power, acquiring an explicit model becomes difficult. In this article, the behavioral model of direct methanol fuel cell is proposed with satisfactory accuracy, using only input/output measurement data. First, using the generated data which are tested on the direct methanol fuel cell, the frequency response of the direct methanol fuel cell is estimated as a primary model in lower accuracy. Then, the norm optimal iterative learning control is used to improve the estimated model of the direct methanol fuel cell with a predictive trial information algorithm. Iterative learning control can be used for controlling systems with imprecise models as it is capable of correcting the input control signal in each trial. The proposed algorithm uses not only the past trial information but also the future trials which are predicted. It is found that better performance, as well as much more convergence speed, can be achieved with the predicted future trials. In addition, applying the norm optimal iterative learning control on the proposed procedure, resulted from the solution of a quadratic optimization problem, leads to the optimal selection of the control inputs. Simulation results demonstrate the effectiveness of the proposed approach by practical data.

Local P-wave tomography is an efficient method to study geologically complex areas where the seismic exploration methods are not ideal for unraveling the shallow crustal heterogeneity due to the great thickness of evaporitic deposits. Despite the complex geological features in the salt-rich DehDasht region, SW Iran, we used more than 11 000 micro-earthquake events, which have been recorded by a temporary seismic network (deployed between October 18th, 2016, and July 1st, 2017), to derive the three-dimensional velocity structure based on the first arrival time. We selected a subset of events (1571 micro-earthquakes) by various strict criteria for our processing, and then the 1D velocity model was calculated by the computer program VELEST. Afterward, the 3D initial model of the inversion procedure with 1.5 km in horizontal and 1 km in depth intervals was parametrized using the calculated 1D model. Finally, the observed data (first arrival P-wave traveltimes and events locations) was inverted with an optimum regularization parameter and iteration using the computer program SIMULPS14. Our tomographic results indicate the DehDasht basin as a relatively low-velocity zone filled out dominantly by the Gachsaran Formation and surrounded by the high-velocity Asmari-Pabdeh-Sarvak Formations. The basin has a bowl shape that is elongated in the NW-SE direction or an oval on a horizontal view. The depth of the basin varies between 3–5 km and contains many folding-faulting systems, which lead to locally low-velocity patches. Moreover, some evaporate deposits, which are overlying the Gachsaran Formation, emerge as a thin low-velocity layer (e.g. Aghajari, etc.).

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