The potential of the Bland-Altman method in chemical engineering

• Bland-Altman plots and the Kruskal-Wallis test applied to chemical engineering research to determine agreement between measurements. • The limitations of regression analysis are explained as it applies to determining the agreement between different measurement devices. • This paper emphasises the applicability of statistical methods traditionally used in medical research to address complex engineering challenges. Statistical methods play a crucial role in the analysis and interpretation of complex data in chemical engineering research. However, parametric statistical approaches may not always suffice in addressing the intricacies of data analysis within design, process modelling and process development. This paper explores the application potential of less conventional statistical methods in the context of chemical engineering. The application potential of these methods will be demonstrated using recent findings from a study conducted on a low-flux flue gas desulphurisation circulating fluidised bed (FGD-CFB). We analysed the solids flux measurements in a novel, low-flux CFB system by using a less conventional statistical technique, the Bland-Altman method, and verifying the findings using the Kruskal-Wallis test. This statistical analysis validated a non-isokinetic solids sampling technique for low-flux dry and semi-dry FGD-CFBs. This case study demonstrates the applicability of this statistical method within chemical engineering, highlighting its potential beyond its traditional use in medical research. Importantly, the focus of this contribution lies not on the specific findings of the case study, but on the methodologies employed therein. This paper aims to be an educational piece to inform the chemical engineering researcher on how and why this less-used-within-engineering statistical method may be applied. It highlights the potential of utilising statistical approaches that are typically used outside the context of engineering in addressing practical engineering challenges. This paper emphasises the applicability of this statistical method in providing valuable insights into complex engineering systems. It is guided by a systematic inquiry aimed at assessing the statistical methods’ suitability for different types of data sets. By embracing this method, chemical engineers can potentially unlock new insights for research and development, facilitating innovation, sustainability, and advancement in the field.