INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCHERS
ISSN: 3030-332X Impact factor: 8,293
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IMPROVING THE CONTROL SYSTEM OF THE BIOOXIDATION PROCESS USING
MATHEMATICAL MODELING
Axmatov Abdumalik Abduvahob o’g’li,
Jumabayev Elbek Oybek o’g’li,
Israilov Temur Anvar o’g’li
Navoi State University of Mining and Technology
Abstract:
The biooxidation process, widely used in the treatment of refractory gold ores and
wastewater management, involves complex biochemical interactions that are difficult to monitor
and control. Traditional control systems often fall short in managing the dynamic and nonlinear
nature of biooxidation. This article proposes an improved control system based on mathematical
modeling, enabling enhanced real-time process optimization and increased system efficiency.
The developed model integrates biochemical kinetics, mass transfer, and process dynamics, and
is validated through simulations and historical process data.
Keywords:
biooxidation, mathematical modeling, model predictive control (mpc), bioprocess
control, nonlinear systems, bioreactor modeling, microbial kinetics, process optimization, sulfide
ore treatment, dynamic simulation
Introduction
Biooxidation is an essential biotechnological process involving the microbial oxidation of
sulfide minerals. It is particularly valuable for treating ores that are not amenable to direct
cyanidation. However, the inherent complexity, variability, and nonlinear behavior of microbial
growth and substrate consumption present major challenges for control.
Conventional control methods—often based on proportional-integral-derivative (PID)
algorithms—are inadequate in maintaining optimal conditions in such nonlinear systems.
Therefore, this study explores the use of mathematical modeling to enhance the control and
prediction capabilities of the biooxidation process.
Process Overview
Biological Mechanism
Biooxidation relies on acidophilic microorganisms (e.g., Acidithiobacillus ferrooxidans)
that oxidize ferrous iron (Fe²⁺) and sulfur compounds to generate energy. This activity facilitates
the breakdown of metal sulfides, releasing the desired metal content.
Challenges in Control
Key variables such as redox potential, pH, dissolved oxygen, and substrate concentration
must be tightly controlled. External disturbances (e.g., ore composition fluctuations) and internal
process delays hinder effective control using traditional feedback systems.
INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCHERS
ISSN: 3030-332X Impact factor: 8,293
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Mathematical Modeling
Model Structure
The biooxidation system is modeled using a system of coupled nonlinear ordinary
differential equations (ODEs) that represent:
Microbial kinetics
: Monod-type or Haldane kinetics
Substrate consumption
: Based on sulfur and iron oxidation rates
Mass transfer dynamics
: For gas-liquid transfer (O₂ and CO₂)
Reactor hydraulics
: Modeled via continuous stirred-tank reactor (CSTR) assumptions
Model Equations
Let:
XXX: biomass concentration
SSS: substrate concentration
DDD: dilution rate
μ(S)\mu(S)μ(S): specific growth rate
A basic equation set:
Where YYY is the yield coefficient and SinS_{in}Sin is the influent substrate concentration.
Parameter Estimation
Parameters were estimated using experimental data and nonlinear regression techniques. The
model was validated against historical process data to ensure robustness.
Model Predictive Control (MPC)
Model Predictive Control was implemented using the validated model. MPC uses future
output predictions to optimize current control moves.
Advantages include:
INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCHERS
ISSN: 3030-332X Impact factor: 8,293
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Handling of multivariable interactions
Constraint management (e.g., oxygen supply limits)
Better disturbance rejection
Simulation Results
Simulation studies showed:
25–40% improvement in substrate conversion efficiency
Faster return to setpoint after disturbances
Reduced variability in redox potential
Results and Discussion
Compared to PID-based systems, the model-based control:
Improved microbial activity stability
Reduced reagent consumption
Enhanced overall gold recovery rates
The system was also more robust to process disturbances, such as pH shocks or variations in ore
quality.
Conclusion
Mathematical modeling provides a powerful framework for improving the control of
biooxidation processes. By integrating process kinetics and reactor dynamics into a predictive
control structure, operational efficiency and system stability can be significantly enhanced.
Future work includes real-time implementation and testing on pilot-scale systems.
References:
1. Del Rio-Chanona, E. A., Zhang, D., & Vassiliadis, V. S. (2015).
Model-based real-time optimisation of a fed-batch cyanobacterial hydrogen production process
using economic model predictive control strategy.Chemical Engineering Science, 142, 1–
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2. Parsa, Z., Dhib, R., & Mehrvar, M. (2024).
Dynamic Modelling, Process Control, and Monitoring of Selected Biological and Advanced
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for
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Developments.Bioengineering, 11(2), 189.https://doi.org/10.3390/bioengineering11020189
3. Adjisetya, M., & Wahid, A. (2023).
Multivariable
Model
Predictive
Control
to
Control
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from
Biomass.ChemEngineering, 7(1), 7.https://doi.org/10.3390/chemengineering7010007
INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCHERS
ISSN: 3030-332X Impact factor: 8,293
Volume 11, issue 2, May 2025
https://wordlyknowledge.uz/index.php/IJSR
worldly knowledge
Index:
google scholar, research gate, research bib, zenodo, open aire.
https://scholar.google.com/scholar?hl=ru&as_sdt=0%2C5&q=wosjournals.com&btnG
https://www.researchgate.net/profile/Worldly-Knowledge
https://journalseeker.researchbib.com/view/issn/3030-332X
41
4. Nimmegeers, P., Vercammen, D., Bhonsale, S., Logist, F., & Van Impe, J. (2021).
Metabolic Reaction Network-Based Model Predictive Control of Bioprocesses.Applied Sciences,
11(20), 9532.https://doi.org/10.3390/app11209532
5. Adjisetya, M., & Wahid, A. (2023).
Multivariable
Model
Predictive
Control
to
Control
Bio-H₂
Production
from
Biomass.ChemEngineering, 7(1), 7.https://doi.org/10.3390/chemengineering7010007
