Teaching

I support lectures, labs, software training, and assessment across several courses in modelling, mining engineering, and to some extend, raw materials value chain.

Mineral Resource Geology & Modelling
Geostatistics
Resource Modelling
Economic Geology
Mining
Critical Raw Materials
Resource Classification
Datamine Studio RM

This course introduces the geological, technical, and economic foundations of mineral resources, covering ore deposit styles, critical raw materials for the energy transition, and the full mining value chain from exploration to circularity. It trains students to apply resource-modelling techniques, geostatistics, and exploration methods, culminating in computer-aided mineral resource estimation and evaluation.
Field Exploration Project
Project Assessment
Techno-Economic Analysis
Mining
JORC

This project-based course trains students to design a complete subsurface engineering project—integrating technical, environmental, economic, societal, and ethical constraints—and justify their decisions through a written report and oral presentation. Working largely in teams with limited supervision, students gather and assess relevant data, develop a feasibility study, and demonstrate both hard engineering skills and soft professional skills.
Geo-Resource Module B
Geostatistics
Simulation
Kriging
Uncertainty Quantification
Resource Modelling

This inquiry-based module combines theory, lab work, and fieldwork to train students in characterizing and modelling mineral resources for resource efficiency and a green future. Students plan and execute field campaigns, analyse and model data using advanced geostatistical techniques, evaluate environmental and ethical implications, and communicate findings through reports and presentations.
Geo-Resource in Society
Resource Modelling
Raw Materials Value Chain
Critical Raw Materials

This module focuses on Critical Raw Materials Geo-Resources, teaching students to evaluate subsurface resources, manage geological uncertainties, and integrate geoscience and engineering concepts into project planning. Through scenario-based exercises and a team project, students develop decision-making skills, assess technical challenges, and communicate solutions effectively in reports and presentations.
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Supervision

I provide hands-on supervision in students’ projects/theses, guiding students in applying statistical and mathematical methods to real-world engineering problems. I emphasize reproducible workflows, software proficiency, and effective scientific communication, helping students achieve both academic excellence and practical skill development.

Sequence optimization for the dredge mining of heavy mineral sands:

a meta-heuristic approach
Modelling
Optimization
Mining

A meta-heuristic model is developed to optimize long-term extraction paths for dredge-mined heavy mineral sands, integrating modifying factors and fixed product offtake.

2026
Owen de Ouden

Beyond Diesel in Mining:

A Comprehensive Analysis of Mineral Agnostic Variables Impacting a Zero Emissions Mining Environment
Mining
Optimization
Techno-Economic Analysis
TCO Model
Australia

This thesis investigates the feasibility of deploying BET with AHS using a mineral-agnostic framework, defined as a model that is consistently applicable to commodities such as iron, copper, and nickel.

2025
Aafje L. Houben

Analyzing the relationship between Ferromanganese crust composition and water depth through Collocated Co-Kriging

investigate potential reduction in sampling needed for deep sea resources exploration
Geostatistics
Kriging
Deep Sea Mining
Uncertainty Quantification

This thesis investigates the relationship betweenwater depth and Fe-Mn crust composition and evaluates how incorporating this relationship through collocated co-kriging (CK) can improve resource estimation accuracy and confidence compared to ordinary kriging (OK)

2025
Arjun Prakash Kollamparambil

Modeling Squeezing Effects Using the PLAXIS SHANSEP Module

Application to the Klimaatdijk as a Real-World Case Study
Modelling
Geotechnical Engineering
PLAXIS

This thesis investigates the capability of PLAXIS to model squeezing effects using the SHANSEP (Stress History and Normalized Soil Engineering Properties) approach and compares its performance with analytical methods.

2025
Evy de Bruine

Technical challenges and Cost modelling of an Underground Crushing plant

Modelling
Sensivity Analysis
Techo-economic Analysis
Mining

The thesis examines the feasibility of moving a crushing plant underground to support hydraulic ore transport. It identifies key challenges such as limited space, dust control, and geological uncertainties, and develops layout options and a cost-estimation model to guide early project planning.

2025
Sander van Grootveld

Evaluating the Impact of Geological Trends on Resource Estimation

A study on the a Nickel mine using Datamine RM
Resource Modelling
Sensivity Analysis
Geostatistics
Resource Classification

This report investigates the influence of geological trends on the quality and accuracy of resource estimation at a Nickel mine. The primary aim is to determine whether incorporating geological structures into the modelling process significantly improves the reliability of the resulting block model.

2025
Tom Reurink

Advanced Multivariate Geostatistical Analysis, and its challenges

applied on a Epithermal deposit
Resource Modelling
Geostatistics
Exploratory Data Analysis
Uncertainty Quantification

The thesis applies advanced multivariate geostatistics to an epithermal deposit, clarifying uncertainty in the resource model. By reassessing copper and gold variability, it supports clearer decisions for mine planning and processing.

2025
Travis Hogan

Evaluating the Influence of Geostatistical analysis on Domain Estimation

A Case Study of the Nickel Deposit in Botswana
Geostatistics
EDA
Domain Estimation

On this thesis, the aim was to evaluate to which extent geostatistical methods specifically indicator kriging can enhance or refine the current geologic domaining practices in the Nickel deposit.

2024
Danylo Muravskiy

Bayesian Evidential Learning for Hardness Prediction of Orebodies

Application to the Walker Lake Data Set
Geostatistics
Bayesianism
Uncertainty Quantification

This research minor explores the application of Bayesian evidential learning to predict ore hardness, applying the assumptions on the Walker Lake data set. The study focuses on integrating prior knowledge with observed data to enhance prediction accuracy and quantify uncertainties

2023
Aafje L. Houben
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