Neural network based controllers for the oil well drilling process

Ribeiro, Vanessa de Jesus da Silva; Oliveira, Gabrielle Fontella de Moraes; Cristian, Muara; Martins, André Leibsohn; Fernandes, Lucas; Vega, Márcia Peixoto

doi:10.1016/j.petrol.2019.01.078

Cited by 10 publications

(1 citation statement)

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“…In [23], an ANN-based Fuzzy Logic Controller is proposed to track the optimal set-points of the S NO,2 and S O,5 concentrations. In [24], ANNs model the direct relationships of an oil well drilling process. Later, they have been adopted either to determine the actuation variable of the control process, or to determine the next state of the system before being considered by a controller.…”

Section: Introductionmentioning

confidence: 99%

Industrial Control under Non-Ideal Measurements: Data-Based Signal Processing as an Alternative to Controller Retuning

Pisa

Morell

Vilanova

et al. 2021

Sensors

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Industrial environments are characterised by the non-lineal and highly complex processes they perform. Different control strategies are considered to assure that these processes are correctly performed. Nevertheless, these strategies are sensible to noise-corrupted and delayed measurements. For that reason, denoising techniques and delay correction methodologies should be considered but, most of these techniques require a complex design and optimisation process as a function of the scenario where they are applied. To alleviate this, a complete data-based approach devoted to denoising and correcting the delay of measurements is proposed here with a two-fold objective: simplify the solution design process and achieve its decoupling from the considered control strategy as well as from the scenario. Here it corresponds to a Wastewater Treatment Plant (WWTP). However, the proposed solution can be adopted at any industrial environment since neither an optimization nor a design focused on the scenario is required, only pairs of input and output data. Results show that a minimum Root Mean Squared Error (RMSE) improvement of a 63.87% is achieved when the new proposed data-based denoising approach is considered. In addition, the whole system performance show that similar and even better results are obtained when compared to scenario-optimised methodologies.

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Section: Introductionmentioning

confidence: 99%