Series-connected data-based model to estimate effluent chemical oxygen demand in industrial wastewater treatment process.

Continuous monitoring of chemical oxygen demand (COD) is essential to ensure efficient and sustainable wastewater treatment and regulatory compliance. However, traditional hardware measurements are laborious, infrequent and costly. In this research, a cost-effective real-time alternative is presented.

Sub-Surface Analysis of Grinding Burns with Barkhausen Noise Measurements.

Barkhausen noise (BN) measurements are commonly used for surface characterisation. However, often there is also a need to verify the sub-surface region because detrimental tensile stresses may be present after different manufacturing steps. Especially in a grinding burn, the surface stress may be compressive, but it changes quickly into tensile stress below the surface.

Systematic Data-Driven Modeling of Bimetallic Catalyst Performance for the Hydrogenation of 5-Ethoxymethylfurfural with Variable Selection and Regularization.

Catalyst development for biorefining applications involves many challenges. Mathematical modeling can be seen as an essential tool in assisting to explain catalyst performance. This paper presents studies on several machine learning (ML) methods that can model the performance of heterogeneous catalysts with relevant descriptors.

How to preserve and handle fish liver samples to conserve RNA integrity.

As transcriptomic studies are becoming more and more common, it is important to ensure that the RNA used in the analyses is of good quality. The RNA integrity may be compromised by storage temperature or freeze-thaw cycles, but these have not been well studied in poikilothermic fishes. This work studied the effects of tissue storage time and temperature, and freeze-thaw cycles of tissue and extracted RNA on RNA integrity in brown trout (Salmo trutta L.

Enabling Demand Side Management: Heat Demand Forecasting at City Level.

Implementation of new energy efficiency measures for the heating and building sectors is of utmost importance. Demand side management offers means to involve individual buildings in the optimization of the heat demand at city level to improve energy efficiency. In this work, two models were applied to forecast the heat demand from individual buildings up to a city-wide area.