Moving horizon estimation (MHE) has been applied to an industrial gas phase polymerization reactor to improve estimates of current states and parameters. MHE is compared to Implicit Dynamic Feedback (IDF(TM))(1). With MHE, there is impropved estimation of unmodeled disturbances in the UNIPOL(TM) polyethylene plant. The UNIPOL(TM) technology is licensed by Univation, a joint venture between ExxonMobil and Dow. The polymerization reactor and plant model is a large-scale set of differential and algebraic equations (DAEs) posed in open equation form. The DAE model is converted to algebraic equations by orthogonal collocation and solved with the MHE objective function in a simultaneous optimization. NOVA(TM), an active-set sparse NLP solver, is used to converge the problem that has 46,870 variables, 18 complementarity conditions, and a Jacobian sparsity of 0.01%. This large, sparse optimization problem is initiated every 5 minutes to update the model as new plant measurements become available and prior to the control optimization. The same plant model is used for nonlinear model predictive control (MPC) with 10 manipulated variables (MVs) and 26 controlled variables (CVs). In this case, a significant advantage is that with MHE a simpler rigorous model suffices for the application of nonlinear MPC.