This paper deals with the problem of real-time estimation and identification in batch processes represented by stochastic nonlinear state-space models (SSMs). To address this, the two problems - estimation and identification - are formulated under a common Bayesian filtering framework. In contrast to a continuous process, a batch process is typically operated for a relatively shorter duration, which limits the number of measurements available in a campaign to effectively solve the filtering problem. To overcome the limitations of existing solutions, we propose a two-dimensional dual particle filtering algorithm to solve the nonlinear filtering problem. The proposed algorithm performs filtering both along the time and the batch directions. This allows to combine measurements from multiple short campaigns for improved estimation and identification results. The efficacy of the proposed method is demonstrated on two simulation examples. (C) 2019 Elsevier Ltd. All rights reserved.