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When tapping a blast furnace, a break-through of gas through the taphole at the end of a cast needs to be prevented, both to preserve a healthy state of the taphole and to prevent gas and dust from escaping into the environment. In this paper, two acoustic techniques are presented that can be used to prevent gas emissions from the taphole at the end of a cast. The first approach is by using the spectral analysis of the recorded data, the second approach uses a neural network to recognize the popping sounds that announce the slag–gas interface is approaching the taphole level. It was found that with both methods the end of the cast is detected about 4–2 min before the cast is ended.

The refractory thickness of the blast furnace hearth is a critical parameter for the safe operation of a blast furnace. A replacement of the refractory comes with high costs and, therefore, unnecessary repairs should be avoided. A promising method to estimate the refractory thickness is the impact-echo method. For the interpretation of impact-echo, often, the peaks in the spectrum of the measured signal are associated with the echo from an interface. In this paper, it is shown that this method should be used with care when applied to multi-layered structures. An alternative approach is presented for the calculation of the thickness of multi-layered structures. This method solves the wave equation based on transmission line theory. The formula is checked against 2D finite element analysis and for simple layered structures, good agreement is found. Three cases are simulated using finite element models. Both the impact-echo method and the proposed method overestimate the refractory thickness.

In this paper, we recover the amplitude of a seismic image by approximating the normal (demigration-migration) operator. In this approximation, we make use of the property that curvelets remain invariant under the action of the normal operator. We propose a seismic amplitude recovery method that employs an eigenvalue like decomposition for the normal operator using curvelets as eigen-vectors. Subsequently, we propose an approximate non-linear singularity-preserving solution to the least-squares seismic imaging problem with sparseness in the curvelet domain and spatial continuity constraints. Our method is tested with a reverse-time `wave-equation' migration code simulating the acoustic wave equation on the SEG-AA salt model.