Model predictive control of a rotary kiln for fast electric demand response

Machalek, Derek; Powell, Kody M.

doi:10.1016/j.mineng.2019.106021

Model predictive control of a rotary kiln for fast electric demand response

Journal Article · Tue Sep 24 04:00:00 EDT 2019 · Minerals Engineering

DOI:https://doi.org/10.1016/j.mineng.2019.106021· OSTI ID:1864014

Machalek, Derek ^[1]; Powell, Kody M. ^[2]

Univ. of Utah, Salt Lake City, UT (United States); University of Utah
Univ. of Utah, Salt Lake City, UT (United States)

Rotary kilns require large fans to induce drafts to support transformation of minerals. If fan controls can be modified to respond to rapid changes in electric demand, they can become valuable grid assets. Here, due to their considerable thermal inertia, kilns and the associated fans are traditionally operated continuously in a steady manner to avoid process disruption. However, advanced process control, such as model predictive control (MPC) can allow for the rigid process to be operated flexibly. Ultimately the flexible process can respond to grid demand requirements by ramping the induced draft fan. Process disturbances generated by a quick change in the kiln air flow rate are buffered by adjusting kiln operations using MPC. In this work, a through, dynamic kiln model was developed. On top of the model, a novel MPC algorithm is presented which leverages kiln rotation rate, stone feed rate, and coal feed rate to ensure successful calcination of limestone while the kiln simultaneously plays a crucial role in electric grid regulation. Four scenarios were explored, long and short term duration of both up and down regulation. The MPC algorithm reduced the number of hours of poor stone quality production to 0 during smart grid participation. The economic penalty of the grid responses was reduced by at least 65% using MPC, compared to steady-state optimized operation. For short-term participation, less than two hours, the MPC algorithm would reduce economic penalties by at least 77%. Minimized economic penalties and high stone quality improve the feasibility of smart grid participation.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Utah, Salt Lake City, UT (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: EE0007712

OSTI ID:: 1864014

Alternate ID(s):: OSTI ID: 2325324
OSTI ID: 1799340

Journal Information:: Minerals Engineering, Journal Name: Minerals Engineering Vol. 144; ISSN 0892-6875

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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42 ENGINEERING
Ancillary services
Calcination
Dynamic optimization
Model predictive control
Rotary kiln
Steady-state optimization

Model predictive control of a rotary kiln for fast electric demand response

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