Engineering applications such as fluidised bed reactors or powder handling in 3D printing contain a large number of particles. Indeed this number is too large to be able to track each particle individually by a pure Lagrangian approach. Recently, the multi-phase Particle-in-Cell (MP-PIC) has received considerable attention for predicting the dynamics of a large number of particles and has proven its applicability to engineering systems. However, predicting also thermal conversion for these arrangements is largely in its infantile stage. Therefore, this study proposes a statistical approach for the thermal conversion of a large number of particles for fluidised bed reactor processes. The proposed study will employ the MP-PIC technology available in OpenFoam software and enhance it by appropriate statistical methods to describe thermal conversion such as gasification of large number of particles in a fluidised bed. The work will benefit from the existing know-how of the research group and the compatibility between the XDEM as an Euler-Lagrange approach and OpenFoam code. Hence, the project will focus on the development of the statistical methods. Those methods will be tested and optimized with the results from the traditional Euler-Lagrange approach in XDEM. In addition the method is validated to proof its predictive capability by collaborating with industrial partner Soil-Concept and AddUp Solutions. Soil-Concept operates a pilot plant for a fluidised bed reactor and AddUp Solutions develops solutions for 3D printing. Based on this concept a deeper understanding of the underlying chemo-physical processes is obtained that allows for an improved design and operation.