Modern chemical plants are characterized by their large-scale, strong interactions and the presence of constraints. With its ability to systematically handle these issues, distributed model predictive control (DMPC) is a promising approach for the control of such systems. However, the problem of how to efficiently solve the resulting distributed optimization problem is still an open question. This paper develops a novel fast DMPC approach based on a distributed active set method and offline inversion of the Hessian matrix to efficiently solve a constrained distributed quadratic program. A dual-mode optimization strategy based on the value of unconstrained optimal solution is developed to accelerate the computation of control action. The proposed method allows for the optimization to be terminated before convergence to cope with the fast sampling periods. Furthermore, a warm-start strategy based on the solution of the previous sampling instant is integrated with the approach to further improve convergence speed. The approach is highly parallelized as constraints can be checked in parallel. The approach is demonstrated using an academic example as well as a chemical process network control. (C) 2014 Elsevier Ltd. All rights reserved.