Decarbonisation Technology - November 2024 Issue

Quantify and reduce risk for acceleration of new projects Understanding and reducing the uncertainty associated with new sustainability projects will close the gap between actual and targeted levels of investment

Ana Khanlari and Ron Beck Aspen Technology

I n the fast-evolving landscape of sustainable chemical manufacturing, vetting projects and making final investment decisions can present difficult choices. Some of these complexities include the flurry of emerging technologies, conflicting objectives, uncertain future economics, and re-shoring momentum. A lack of delivered assets operating at scale to benchmark against can hinder the progress of any new project. Decisions made along the lifespan of the project without considering future changes might be counterproductive or even put the project in jeopardy. Having a complete, quantitative system view to show the trade-offs of every decision can bring a fact-based measure of risk for the project’s owners. Through such a holistic system model, owners can examine end-to-end dependencies, evaluate market and financial projections, and forecast future returns and performance without being caught off-guard. Today’s decarbonisation and circularity imperatives are driving the industry towards new modes of operation. Electrification and utilisation of dedicated renewable power are decarbonisation steps that come with embedded stochastic and cyclical patterns of solar and wind. Additionally, new value chains (such as plastics recycling and renewable-based chemical synthesis) introduce uncertainties like quantity and quality of raw material supply. Other uncertainties include weather, future material pricing, supply chain disruptions, and a dizzying array of optionalities in putting together new end-to-end systems. A probability-based system model can account for these uncertainties to predict future constraints and offer mitigating solutions.

Monte Carlo simulation is a statistical approach that uses repeated random sampling to predict a range of potential results. The method takes its name from the Monte Carlo Casino frequented by physicist Stanislaw Ulam’s (method inventor) uncle. This method was introduced effectively during World War II to improve decision-making under uncertain conditions. Since then, the application of the Monte Carlo method has expanded to all fields of science, finance, engineering, and project management. Conducting a Monte Carlo analysis on the most likely future pricing of material and labour is an established application in large capital project biddings. Augmenting this statistical approach with a model of the ‘connectivity’ of a system (understanding interrelationships like materials flow, and electricity) creates a sophisticated tool to rigorously evaluate a wide range of investment and operational alternatives across a system. There are many opportunities for de-risking new projects using a Monte Carlo systems assessment. A system’s lifecycle cost can be minimised, production maximised, and availability predicted. For example, by considering equipment failure rates, maintenance costs, and spares availability, a ‘connected’ Monte Carlo model can improve the system’s reliability. The efficiency and productivity of the design can be improved by identifying optimal places to put redundancies, bypasses, and intermediate storage elements. Lifecycle emissions can be minimised by leveraging renewable energy sources while assigning emissions an opportunity cost in the model. Finally, Capex can be minimised by predicting performance and throughput changes, maintenance intervals, and