(29e) Real-Time Coordinating of Multiple Conveyor-Belts and Multi-Quality Stockpiles for Tripper Car Positioning in the Mining Field | AIChE

(29e) Real-Time Coordinating of Multiple Conveyor-Belts and Multi-Quality Stockpiles for Tripper Car Positioning in the Mining Field

Authors 

Menezes, B. C. - Presenter, Hamad Bin Khalifa University, Qatar Foundation
Kelly, J. D., Industrial Algorithms
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Brenno Castrillon Menezes Brenno Castrillon Menezes 4 4943 2019-04-12T11:53:00Z 2019-04-12T19:56:00Z 4 973 5549 46 13 6509 16.00

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We
propose an online dynamic, discrete and nonlinear scheduling optimization as a
real-time hybrid model predictive controller applied to a shuttle-conveyor / tripper
car intermittently delivering crushed-ore containing copper, iron, etc., to
several stockpiles in the mining field. In the proposed cyber-physical system
(CPS), the coordination of sequencing and timing of shuttle-conveyors / tripper
car positions that forms multiple quality stockpiles of crushed-ore is
addressed. The problem considers solids of crushed-ore with different
mineralogy and metal content from crushing units upstream of the CPS
environment. These diverse crushed-ore are mixed in a simultaneous feeding from
shuttle-conveyors onto stockpiles to match feed quality needs into the grinding
processing steps downstream of the CPS. Figure 1 shows the schematics of tripper cars and shuttle-conveyors with
different crushed-ores to be fed onto stockpiles. By the figure representation,
there will be a change of mixtures of two types of crushed-ore per stockpile.
For a better distribution or mixing, the tripper car of the different ore
quality should be fed simultaneously.

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mso-bidi-font-size:10.0pt;mso-fareast-font-family:" times new roman minor-fareast>Figure 1. Tripper car shuttle-conveyor
and crushed-ore stockpile schematics.

12.0pt;margin-left:0in;text-indent:0in">The discrete tripper car positioning in the
CPS operation is re-optimized every 4 minutes for the tripper car moving from
one position to the next on the shuttle-carriage considering the measurements
of the stockpile levels and qualities of the conveyor-belts feeding the
grinding units. The model uses a combination of an
MILP+NLP decomposition [1], [2] with a linear
(LP) approximation for blending the crushed-ore streams for the grinding
process feed quality [3]. A similar proposition is used in the
crude-oil blend scheduling optimization and control [4]. The shuttle-conveyor
is the discrete actuation or manipulated variable depositing the solids onto
each stockpile where we vary the time over each stockpile to automatically
control to a setpoint or target their levels sensed by industrial radar as well
as their quality in terms of mineralogy and metal content. The purpose of the
application is to improve the stockpile level and quality control performance
by automatically adjusting the run-length or up-time of the shuttle-conveyor
tripper car over each stockpile based on real-time level measurement feedback
and the application which is referred to as “smart sweeping” [5].

Advances
in manufacturing toward the Industry 4.0 (I4) age and beyond pushes the
development of autonomous industrial systems to (re-) execute them to achieve
an improved production state with improved economics, efficiency and
effectiveness. For such, the information and communication technologies (ICT),
high-performance computing (HPC) and mechatronics (MEC) are evolving together
with the advances in modeling and solving algorithms (MSA). We explore the MSA
aspects for smart applications in the mining and mineral processing of the
future for the integration of the CPS in study to the downstream grinding
plant. A broad review on such technologies to be applied in industry can be
seen in [6], [7].

Considering
the industrial examples inside the I4 domain of MSA, ICT, HPC and MEC, the
sensing, calculation and actuation (SCA) cycle within minutes can be
established in the mining system by the stabilization of all ground bases [7].
In this case, the MEC support for straightforward shuttle-conveyor mechatronics
technology is relatively mature. Only after the advances in the MSA pillar, see
Brunaud et al. [8], by reducing large discrete optimization calculus from hours
to a few minutes or seconds by better formulation of the superstructure of the
material flow networks of the unit-operations, we argue that the mining system
may be optimized and controlled  in a
complete I4 manufacturing infrastructure. The proposition in this work can
extend the CPS in study (Figure 1) to further complex operations by including
quality issues or even add more equipment to the CPS as the grinding and
downstream separation and concentration units.

[1]
Menezes BC, Kelly JD, Grossmann IE. (2015) Phenomenological decomposition
heuristic for process design synthesis of oil-refinery Units. Computer Aided
Chemical Engineering, 37, 1877-1882.

[2] Kelly JD,
Menezes BC, Engineer F,
Grossmann IE. (2017). Crude-Oil Blend Scheduling Optimization of an
Industrial-Sized Refinery: A Discrete-Time Benchmark. In
Foundations of Computer Aided Process Operations, FOCAPO, Tucson, AR, United
States, 10-13 January.

[3] Kelly JD, Menezes BC,
Grossmann IE. (2018). Successive LP
Approximation for Non-Convex Blending in MILP Scheduling Optimization using Factors
for Qualities in the Process Industry OLE_LINK98">
. Industrial Engineering Chemistry
Research, 57 (32), 11076-11093.

[4]
Franzoi RE, Menezes BC, Kelly JD, Gut JAW. (2018). Blend Scheduling
Optimization Using Factors for Qualities in Cascaded Distillation Towers in
Crude-Oil Refineries, In: São Paulo: Blucher. 1233-1236.

[5]
Kelly JD, Menezes BC. (2019). Automating a Shuttle-Conveyor for Multi-Stockpile
Level Control. In 29th European Symposium on Computer-Aided Process Engineering
(ESCAPE), Eindhoven, The Netherlands, Jun 16-19.

 [6] Menezes BC, Kelly JD, Leal AG, Le Roux GC.
(2019). Predictive, prescriptive and detective analytics for smart
manufacturing in the information age. In 12th IFAC Symposium on Dynamics and
Control of Process Systems (DYCOPS), Florianópolis,
Brazil, Apr 23-26.

[7]
Menezes BC, Kelly JD, Leal AG. (2019). Identification and Design of Industry
4.0 Opportunities in Manufacturing: Examples from Mature Industries to
Laboratory Level Systems. In 9th IFAC Conference on Management, Modeling and
Control Systems (MIM), Berlin, Germany, Aug 28-30.

[8]
Brunaud B, Amaran S, Bury S, Wassick
J, Grossmann IE. (2019). Batch scheduling with quality-based changeovers.
Computer and Chemical Engineering. Just Accepted.