Reduced-space barrier NLP algorithms are particularly useful for optimization of large structured systems with few degrees of freedom. Such optimization algorithms are often applied on process models developed within equation oriented process simulators. By partitioning the search direction into tangential and normal steps, these methods can exploit the structure of the equality constraints and adjust the remaining degrees of freedom in a lower dimensional space. Moreover, as shown in previous work, the barrier approach extended with a novel filter linear search algorithm has global and fast local convergence properties. However, convergence properties of the reduced-space barrier algorithm require regularity assumptions. In particular, the method may fail in the presence of linearly dependent active constraints. To deal with these questions, we modify the reduced-space barrier method in two ways. First, as the filter line search requires a feasibility restoration step, we develop and analyze an improved algorithm for this step, which is tailored to the reduced-space method. In addition, a dimension change procedure is proposed to address decomposition of problems with linearly dependent constraints. Finally, both approaches are implemented within a reduced-space version of IPOPT and numerical tests demonstrate the performance of the proposed modifications. (C) 2010 Elsevier Ltd. All rights reserved.