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The use of low-emission combustion technologies in power boilers has contributed to a significant increase in the rate of high-temperature corrosion in boilers and increased risk of failure. The use of low quality biomass and waste, caused by the current policies pressing on the decarbonization of the energy generation sector, might exacerbate this problem. Additionally, all of the effects of the valorization techniques on the inorganic fraction of the solid fuel have become an additional uncertainty. As a result, fast and reliable corrosion diagnostic techniques are slowly becoming a necessity to maintain the security of the energy supply for the power grid. Non-destructive testing methods (NDT) are helpful in detecting these threats. The most important NDT methods, which can be used to assess the degree of corrosion of boiler tubes, detection of the tubes’ surface roughness and the internal structural defects, have been presented in the paper. The idea of the use of optical techniques in the initial diagnosis of boiler evaporators’ surface conditions has also been presented.

Part 4: Optimization, TuningInternational audienceOur aim is present the methodology of simulations for repetitive processes and tuning control systems for them in the presence of noise. This methodology is applied for tuning a laser power control system of the cladding process. Even the simplest model of this process is nonlinear, making analytical tuning rather difficult. The proposed approach allows us to select quickly the structure of the control system and to optimize its parameters. Preliminary comparisons with experimental results on a robot-based laser cladding systems are also reported. These comparisons are based on the temperature measurements, observations by a camera and IR camera

We propose a rule‐based method of spike detection and suppression method. This method is an extension of the jump detector that was proposed by the second author, M. Pawlak and A. Steland. Its elementary properties are established, and the example of application for a laser power control in a 3‐dimensional additive manufacturing process is discussed.

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We consider a rather general problem of nonparametric estimation of an uncountable set of probability density functions (p.d.f.’s) of the form: f ( x ; r ) , where r is a non-random real variable and ranges from R 1 to R 2 . We put emphasis on the algorithmic aspects of this problem, since they are crucial for exploratory analysis of big data that are needed for the estimation. A specialized learning algorithm, based on the 2D FFT, is proposed and tested on observations that allow for estimate p.d.f.’s of a jet engine temperatures as a function of its rotation speed. We also derive theoretical results concerning the convergence of the estimation procedure that contains hints on selecting parameters of the estimation algorithm.

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