NOPR Collection:http://nopr.niscpr.res.in/handle/123456789/671802026-10-09T21:54:42Z2026-10-09T21:54:42ZProcessing of ochre from Daitari Iron ore mines, Singbhum Craton, Eastern India for optimum utilisationKumar Sahoo, JayantNayak, DeepakMishra, PatitapabanI. Angadi, ShivakumarKhaoash, SomnathKumar Mohapatra, Birendrahttp://nopr.niscpr.res.in/handle/123456789/671902026-01-23T11:13:04Z2025-10-01T00:00:00ZTitle: Processing of ochre from Daitari Iron ore mines, Singbhum Craton, Eastern India for optimum utilisation
Authors: Kumar Sahoo, Jayant; Nayak, Deepak; Mishra, Patitapaban; I. Angadi, Shivakumar; Khaoash, Somnath; Kumar Mohapatra, Birendra
Abstract: Ochre, a naturally occurring powdery material, has been commonly appeared associated with different iron ore morphotypes
in many iron ore deposits of the Singhbhum Craton, eastern India. It appears in yellow, red, gray and black colours.
The present paper has described the characteristics of ochre occurring in the Daitari Iron Ore Mines in Odisha, India, and
has discussed its processing for sustainable iron making and optimum utilization. Usually, ochre shows poor iron value, fine
size and therefore being considered as a waste. However, in the present set-up, the ochre sample has shown ~60% Fe, 4%
combined Al₂O₃ + SiO₂ and ~10% LOI, contributed by hematite, goethite and limonite phases. Though compositionally it
appears to be a good candidate for iron making, it has remained unsuitable as blast furnace feed due to its fine particle size
and high LOI content.
Attempts have been made to convert this powdery ochre to a lumpy form by adopting the pelletization technique. The
sample below 150 μm size has been mixed thoroughly with three different charge mixes (bentonite, limestone and coke
fines) and pellets have been prepared in a laboratory-scale disc pelletizer., Three sets of samples have been prepared with
variable bentonite contents (0.5, 0.75 and 1%) keeping fixed limestone and coke amount. The green pellets from the
pelletizer have been exposed to drop test and Green Compressive Strength (GCS), followed by thermal indurations at 1100,
1200 and 1300 °C. The porosity (%) and Cold Crushing Strength (CCS) (kg/pellet) of the indurated pellets have shown
variations at three different temperatures. Pellets prepared with 1% bentonite content and indurated at 1300 °C have given
22% porosity and 255 kg/pellet CCS. XRD, optical and electron microscopy studies have revealed that recrystallization of
hematite during induration has enhanced pellet strength and properties, while fayalite and calcium ferrite formation has
provided stability through slag bonding. Pellets indurated at 1300 °C have shown Fe enrichment from ~60% to ~67% with
~3.2% combined Al₂O₃ + SiO₂, making them suitable as blast furnace feed. If this technique is adopted, an appreciable
quantity of ochrous waste accumulated at mine sites can be converted to wealth
Page(s): 527-5352025-10-01T00:00:00ZInnovations in water quality management using machine learning approachesBharadwaj, ShivangiKumar Gupta, AshokKumar Sahu, Anilhttp://nopr.niscpr.res.in/handle/123456789/671892026-01-23T11:04:36Z2025-10-01T00:00:00ZTitle: Innovations in water quality management using machine learning approaches
Authors: Bharadwaj, Shivangi; Kumar Gupta, Ashok; Kumar Sahu, Anil
Abstract: Groundwater contamination has been posing a significant threat to sustainable water resource management, particularly
in industrialized and urbanized regions. This research has introduced a novel, data-driven framework that integrates machine
learning, statistical data analysis, and feature optimization to evaluate and forecast groundwater quality. Analytical results of
488 groundwater samples had been tested, and four feature reduction scenarios had been implemented using Pearson
correlation to evaluate predictive performance with minimal input variables. Statistical analysis has highlighted elevated
levels of parameters such as Electrical Conductivity, Chloride, Magnesium, and Total Hardness, exceeding permissible
limits, and have been causing most samples to be unsuitable for consumption without treatment. To enhance groundwater
monitoring and reduce laboratory testing costs, six machine learning algorithms, K-Nearest Neighbors, Support Vector
Machine, Decision Tree, Random Forest, XGBoost, and Artificial Neural Network, have been used to predict the Weighted
Arithmetic Water Quality Index. Model accuracy had been tested using statistical metrics such as R², RMSE, MAE, MAPE,
and CRMSE, with effectiveness assessed using Taylor diagrams. ANN exhibited the highest accuracy even when using a
single input (K), while SVM maintained consistent reliability with only two inputs (Mg and K), providing a cost-effective
monitoring solution. Validation with 70 independent datasets has confirmed the robustness and applicability of the
suggested methodology. The study has presented an innovative modeling strategy that has substantially decreased laboratory
testing needs while preserving predictive reliability. Additionally, it has offered practical implications for scalable,
cost-effective deployment in areas with water scarcity or insufficient dataset.
Page(s): 536-5512025-10-01T00:00:00ZRutting and fatigue performance of high-dosage crumb rubber modified bitumenKumar, GajendraK Tomar, RAhmad Kidwai, Farhanhttp://nopr.niscpr.res.in/handle/123456789/671882026-01-23T11:02:10Z2025-10-01T00:00:00ZTitle: Rutting and fatigue performance of high-dosage crumb rubber modified bitumen
Authors: Kumar, Gajendra; K Tomar, R; Ahmad Kidwai, Farhan
Abstract: This study investigates the effects of crumb rubber (CR) modification on the rheological and performance
characteristics of asphalt binders, with a focus on rutting and fatigue resistance. Modified binders were prepared with
CR contents 10%, 15%, 20% and 24% by weight of viscosity grade-30 (VG-30) binder. These binders are evaluated
for their rheological properties, including Multiple Stress Creep Recovery (MSCR), Zero Shear Viscosity (ZSV),
Shenoy parameter, and Linear Amplitude Sweep (LAS) testusing the Dynamic Shear Rheometer (DSR). The
addition of CRhassignificantly increased the complex shear modulus (G*) and storage modulus (G′) by up to 3.1
and 30.3 times respectively, while reduced the phase angle (δ) by up to 34.6°, indicating improved stiffness and
elasticity. Enhanced values of G*/sin δ and Shenoy parameter haveincreased by 5.34 and 10.4 times respectively for
CR24 binder demonstrating improved rutting resistance. MSCR results have shown thatpercent recovery increased
from -0.2% to 75.1%, and non-recoverable creep compliance (Jnr) decreased from 2.81 to 0.07 kPa⁻¹. The Rutting
Resistance Index Ratio (RRIR) has been effective in evaluating crumb rubber modified bitumen (CRMB)
performance with Jnr showing the highest sensitivity to CR content, establishing it as robust indicator of rutting
resistance. Fatigue analysis has revealed that the binder with 20% CR has offered the best balance between fatigue
resistance and strain tolerance, identifying it as the optimal dosage. A strong inverse correlation with r-square value
0.91 has been found between elastic recovery (ER-DSR) and Jnr, and a moderate positive correlation with
r-square value 0.68 has been found between ER-DSR and fatigue life (Nf), highlighting the interconnected nature of
elasticity, rutting resistance, and fatigue performance.
Page(s): 552-5612025-10-01T00:00:00ZComparative assessment of airport pavement condition using PCI and ACN-PCN methodsNoori, HamidSarkar, Rajuhttp://nopr.niscpr.res.in/handle/123456789/671872026-01-23T10:59:24Z2025-10-01T00:00:00ZTitle: Comparative assessment of airport pavement condition using PCI and ACN-PCN methods
Authors: Noori, Hamid; Sarkar, Raju
Abstract: A scientific approach is essential for evaluating pavement surface conditions at the network level. The prime objective of
airport pavement in terms of functional condition analysis is to focus on the current and future pavement conditions. The
Pavement Condition Index (PCI) is a well-known method widely used to assess the surface conditions of airport pavements.
The aircraft movement from the Runway while take-off and landing or taxiing at the taxiway and parking at aprons induces
a high magnitude of repetitive loads that create excessive stresses due to which pavement layers are affected and distress
appears on the surface of the pavement. This study has been computed the analysis of the distress and traffic measurement to
evaluate pavement condition index (PCI), Structural condition index (SCI), and FOD index. In addition, the aircraft
classification number and pavement classification number (ACN-PCN) methods have been applied to all sections of airport
branches to estimate the structural bearing capacity of the airport pavement network. By adopting the traditional method of
the PCI, the relationship between the pavement condition index and pavement classification number has been studied.
Finally, based on the combined rating index, a treatment methodology has been proposed for the improvement of the
existing critical pavement section by using a decision tree (DT).
Page(s): 562-5802025-10-01T00:00:00Z