Modelling and on-line estimation of zinc sulphide precipitation in a continuously stirred tank reactor

In this paper the ZnS precipitation in a continuously stirred tank reactor (CSTR) is modelled using mass balances. The dynamics analysis of the model reveals that the ZnS precipitation shows a two time-scales behaviour with inherent numerical stability problems, which therefore needs special attention during implementation. The mass balance model allows the design of an on-line estimator of the zinc concentration and precipitation rate, given only wet-chemical measurements of the sulphide concentration and of the pH in the reactor. The on-line estimation algorithm, also called observer or software sensor, is tested in a simulation experiment with a block disturbance showing numerically stable results. Furthermore, on the basis of real-world experimental data, the observer shows an appropriate performance. The estimation procedure offers good possibilities to be applied for other metal removal processes.

[1]  A. Veeken,et al.  Selective Precipitation of Heavy Metals as Controlled by a Sulfide-Selective Electrode , 2003 .

[2]  A. Heesink,et al.  Effects of reactor type and mass transfer on the morphology of CuS and ZnS crystals , 2005 .

[3]  Peter C. Young,et al.  Recursive Estimation and Time Series Analysis , 1984 .

[4]  J. Weijma,et al.  Control of the sulfide (S2-) concentration for optimal zinc removal by sulfide precipitation in a continuously stirred tank reactor. , 2003, Water research.

[5]  K. Keesman.  State and parameter estimation in biotechnical batch reactors , 2000 .

[6]  G. Esposito,et al.  Use of biogenic sulfide for ZnS precipitation , 2006 .

[7]  Dynamic Modelling and Process Control of ZnS Precipitation , 2006 .

[8]  J. D. Stigter,et al.  On adaptive optimal input design: A bioreactor case study , 2006 .

[9]  M. Zeitz.  The extended Luenberger observer for nonlinear systems , 1987 .

[10]  C. Schultz,et al.  Biologically produced sulphide for purification of process streams, effluent treatment and recovery of metals in the metal and mining industry , 2006 .

[11]  J. D. Stigter,et al.  Optimal parametric sensitivity control for a FED batch reactor , 2001, 2001 European Control Conference (ECC).

[12]  J. D. Stigter,et al.  Optimal parametric sensitivity control for the estimation of kinetic parameters in bioreactors. , 2002, Mathematical biosciences.

[13]  K. Keesman,et al.  Selective precipitation of Cu from Zn in a pS controlled continuously stirred tank reactor. , 2009, Journal of hazardous materials.

[14]  Denis Dochain,et al.  State and parameter estimation in chemical and biochemical processes: a tutorial , 2003 .

[15]  A. Heesink,et al.  Crystallization kinetics of ZnS precipitation; an experimental study using the mixed‐suspension‐mixed‐product‐removal (MSMPR) method , 2004 .

[16]  K. Keesman,et al.  Monitoring ZnS Precipitation: Estimation, Error Analysis and Experiment Design , 2009 .

[17]  D. Luenberger.  An introduction to observers , 1971 .

[18]  J. P. Norton,et al.  An Introduction to Identification , 1986 .

[19]  On-line estimation of the dissolved zinc concentration during ZnS precipitation in a CSTR , 2007 .

[20]  R. Govind,et al.  Advances in biotreatment of acid mine drainage and biorecovery of metals: 1. Metal precipitation for recovery and recycle , 2003, Biodegradation.