## 1 Introduction  
In recent years, the electricity generation, supplied to society and industry (predominately in Europe), has been changing from fossil fuel to renewable energy sources such as biomass with the aim of reducing carbon dioxide (CO\({}_{2}\)) gas emissions [1]. As part of this drive for renewable energies, companies such as Drax power burn around 7 million tonnes of wood pellets every year. One of the largest challenges for plant operators is the suppression of dust and minimising health and safety risks during biomass handling and storage operations. In particular, one major threat for the plant operators handling fine combustible dust is the potential for dust explosions. Dust explosions can destroy plant infrastructure and can lead to loss of life. These explosions can occur when an ignition source, oxygen and fuel, such as airborne dust, is present in a confined space [2]. Plant operators install dust suppression systems in biomass handling and storage facilities to control risks of dust explosions and minimise the entrainment of dust into the air. One driving force for dust entrainment into the air is secondary air currents which are driven by a falling material stream during the silo filling process. The secondary air currents carry the fines away from the central feeding jet. The effect is called air current segregation (ACS) and was studied intensively in experiments and simulations [3]. ACS was experimentally evaluated in bench scale testers and results were compared to CFD simulations. Yet, the direct comparison of data was not possible because the accessible information in simulations was vastly different to the accessible data in experiments, which was restricted by measurement techniques and equipment. This paper aims to provide a methodology for evaluating dust concentration ratios in cylindrical silos and for comparing CFD simulations with experimental results. The objective is to test the suitability of a MATLAB based algorithm for converting optical intensity measurements into dust concentration ratios for different reference materials and material flow rates. The dust concentration ratios are obtained by converting the optical intensity data (reflection of light from particles) to colour maps. The colour map contains relevant optical particle concentration information which is visualised in a contour plot as shown in Figure 1.  
To be able to compare optical particle concentrations, a dimensionless number [\(k\)] here defined as \"Concentration Rank Factor\" (CRF) is introduced. CRF is calculated using arbitrarily selected positions in the bench scale silo such as the concentration near the silo wall (\(C_{W}\)) and the highest optical particle concentrations (\(C_{H}\)) as shown in Equation (1). CRF can also be applied to industrial sized silos where (\(C_{W}\)) can be the dust/ material sampling point and (\(C_{H}\)) is the highest optical concentration measured in the silo. \[k=\frac{C_{W}}{C_{H}} \tag{1}\]  
This research builds on previous work modelling multiphase flows for industrial applications [4]. However, previous research has been limited to transfer stations [5] and underground mining [6; 7; 8] which used intrusive sampling methods such as dust monitors and collection trays similar to that used by [PERSON] et al. [9]. Researchers, such as [PERSON] et al. [10] have used numerical simulation (CFD) to predict dust concentration levels in cylindrical storage silos. This study investigated the dust distribution near the central filling stream during silo filling and identified areas of high concentrations close to the explosion limit of the given material. The challenges faced by [PERSON] et al. [10] were that experimental data did not contain a high enough resolution to match the simulation work. This made a direct comparison between experiments and simulations difficult. To obtain higher resolution data, researchers such as [PERSON] and [PERSON] [11; 12] used laser-based optical methods to measure dust concentrations in enclosed spaces.