Research

My research advances industrial ecology by making life cycle assessment more useful before technologies, production systems, and infrastructure choices become locked in. I develop engineering-informed, prospective, and spatially differentiated models that connect technical evidence with decisions about design, scale-up, manufacturing, location, energy supply, facility integration, and operation.

Environmental performance is not a fixed property of a technology. It depends on process design, scale, facility operation, location, energy systems, and time.

Across pharmaceuticals, cleanrooms, sanitation systems, carbon use, and medical technologies, my published work examines the conditions under which an apparent environmental advantage remains credible during development and deployment.

Research Contribution

Conditional life cycle performance

My work moves beyond static comparisons of environmental footprints. Pharmaceutical synthesis studies identify the influence of solvents, process routes, energy, and scale. Cleanroom research tests regional and future electricity and climate pathways. Sol-Char combines suitability screening with alternative manufacturing and transport scenarios. Ex-ante assessment of carbon nanotube production evaluates an emerging process before commercial lock-in. These cases demonstrate that life cycle conclusions are conditional on engineering and deployment context.

Engineering-informed foreground models

My environmental engineering background allows me to work inside the foreground system, not only around it. I use mass and energy balances, process and equipment data, operating schedules, primary measurements, material characterization, and targeted experiments when they can reduce decision-critical uncertainty. The goal is to keep models physically credible as technologies move from laboratory or pilot systems to production, facilities, and delivered services.

From assessment to deployment strategy

I examine where, when, and under what operating conditions a technology can deliver robust benefits. Prospective background systems represent future energy and material production. Spatial data represent location and resource conditions. Scenario, uncertainty, and techno-economic analysis test whether recommendations remain stable across manufacturing strategy, scale, timing, logistics, utilization, and operational integration. The intended output is decision support for technology developers, facility operators, industrial partners, healthcare organizations, and public agencies.

Research Approach

1. Build credible engineering evidence. Define the service, reconcile mass and energy flows, and collect primary data for parameters that can alter a decision.
2. Anticipate development and deployment. Parameterize scale, location, technology maturity, facility operation, supply chains, and future energy systems.
3. Test decision robustness. Use scenarios, uncertainty, sensitivity, and economic assessment to identify stable choices, thresholds, and evidence gaps.

Computational modelling is the core platform. Targeted measurements, small-scale experiments, device disassembly, or material and process characterization can close important foreground-data gaps. This evidence loop allows environmental assessment to accompany technology development instead of entering only after a design has matured.

Application Domains

Sustainable healthcare systems

Sustainable healthcare is the principal application domain. At Erasmus Medical Center (Erasmus University Rotterdam), I lead sustainability modelling for ECO-PATH, which represents hospital facilities, energy-intensive equipment, products, organizational workflows, resource use, and clinical services as connected modules. The purpose is to support decisions that must account for environmental burden, clinical value, cost, equity, and implementation feasibility.

Sustainable pharmaceutical manufacturing

My pharmaceutical research evaluates process routes, solvent and energy use, waste, cost, green chemistry options, environmental KPIs, and scale-up across laboratory, pilot, and industrial systems. This work also draws on two years of industry experience.

Medical devices and controlled facilities

I assess medical devices, clinical consumables, reusable systems, sterilization and disinfection workflows, cleanrooms, and other controlled environments. These cases connect product design with equipment use, facility services, procurement, logistics, safety, and end-of-life management.

Comparative deployment cases

Sanitation, CCUS, renewable-energy communities, circular manufacturing, and carbon utilization provide comparative cases for testing spatial and temporal deployment methods beyond healthcare. They help distinguish reusable industrial ecology methods from conclusions that are specific to one sector.

Selected Programs and Leadership

• PROGENY: Sustainability Analysis Lead for prospective environmental assessment of emerging bionic-device technologies and their fabrication systems.
• LIFE-GREENAPI: Sustainability Analysis Lead across laboratory, pilot, and industrial scales for greener pharmaceutical process development.
• STORCITO: CCUS Technology Lead for technical and sustainability assessment of rural carbon-management pathways.
• RENvolveIT: Project Coordination Lead for an interdisciplinary toolbox supporting renewable-energy communities.
• ECO-PATH: Sustainability Analysis Lead for reusable environmental models of hospital facilities, technologies, workflows, and care services.

Methods and Tools

My core methods include prospective, dynamic, and spatially differentiated LCA; life cycle costing and techno-economic assessment; material flow analysis; Scope 1 to 3 accounting; scenario, contribution, uncertainty, sensitivity, and Monte Carlo analysis; and engineering foreground modelling. I build reproducible workflows in Python using Activity Browser and Brightway, supported by process and energy-system models where appropriate.

Machine learning serves defined and validated tasks such as feasibility screening, parameter estimation, process optimization, and surrogate modelling. It supports engineering and life cycle analysis; it does not replace them.

Research Positioning

My academic identity is industrial ecology, my methodological position is engineering-informed prospective assessment, and my principal application domain is sustainable healthcare. Pharmaceuticals, medical devices, hospitals, care services, and controlled facilities provide a coherent research platform. Sanitation, CCUS, renewable energy, and circular manufacturing test the transferability of the methods across other deployment settings.