Screening is a technical simple but still not fully understood process step, which can be used in a wide field of applications to separate bulk materials according to their particle sizes. A severe issue in screening technologies is that particles frequently prevail in moist conditions, due to effects related to the environment, the material or the process. This is often not preventable, although it is not preferred due to attractive forces altering the screening efficiency. For the design of dry screening processes, phenomenological models and detailed particle-based simulation approaches like the discrete element method (DEM) are available. The latter method has recently been extended and validated against experiments to calculate forces caused by liquid bridges formed out between particles or walls close to each other to meet the requirements to tackle real particle systems under moist conditions. In the investigation here, batch screening under the influence of moisture involving different sized glass spheres is investigated numerically with DEM simulations and by using process models. Therein, the related sub-processes stratification and passage as well as the influence of the operating parameters and the liquid amount on the fraction retained per size class are examined. Existing phenomenological process models, which can be applied efficiently for industrial applications due to their short calculation time, are extended to represent batch screening processes under moist conditions for the first time. Therefore, a benchmark is realized in which the fraction retained per size class over time for discontinuous screening under the influence of various amounts of liquid and different mechanical agitations obtained by DEM simulations and process models is compared. In this context, the process models are first adjusted to fit related simulation results and later used in a novel method to predict the outcome of screening with different operating parameters and liquid amounts. Thereby, process models, which consider the sub-processes stratification and passage, predict screening results for process parameters requiring interpolation or extrapolation in the investigated range very well. As a consequence, newly derived process models can function as prototypes to be applied in dynamic process simulation frameworks. (C) 2018 Elsevier B.V. All rights reserved.