Available for Work

Engineering Excellence through Advanced Simulation, Data Analysis and Process Optimisation

At ChemDigital, you gain direct access to the expertise of Dr. Kieran Jervis—a highly skilled Chemical Engineer dedicated to optimising and transforming industrial processes through physical modelling and data-driven methodologies.

Every solution is crafted with care. Tailoring frameworks and tools to your unique needs, ensuring maximum value and alignment with your goals. An approach emphasising collaboration, transparency, and delivering tangible outcomes that drive real-world success.

Let's work together to turn complex challenges into streamlined, efficient solutions.

Dr. Kieran Jervis

I am Dr. Kieran Jervis, at ChemDigital Ltd. I specialise in delivering innovative digital solutions for chemical process simulation, data analysis, and optimisation. With a PhD in Chemical Engineering from the University of Leeds and three years of professional experience in digital science and engineering, I blend advanced technical expertise with a passion for solving complex industrial challenges.

My career has been shaped by a commitment to designing intuitive, scalable, and impactful solutions. I have developed bespoke simulation frameworks, streamlined data workflows, and optimised processes using Python and cutting-edge analytical tools. Highlights of my work include creating simulation engines from legacy systems, integrating cloud-based automation solutions, and applying classical machine learning to enhance process efficiency.

Services

At ChemDigital Ltd., I offer a flexible and client-focused approach, delivering high-impact solutions custom-built to your project needs. Working directly with me ensures clear communication and a deep understanding of your challenges.

Digital Transformation & Automation

Optimise and modernise workflows by transitioning legacy systems to cloud-hosted solutions. Whether using Azure Durable Functions and REST APIs or other preferred stack, I deliver practical, tailored improvements that streamline operations, automate key processes, and create scalable, user-friendly systems aligned with your needs and infrastructure.

Advanced Chemical Process Simulation

Design and develop intuitive, scalable simulation frameworks in Python that provide actionable insights into system dynamics, performance, and efficiency. These custom solutions help optimise your processes, resolve complex challenges, and support strategic decision-making with precision.

Data Analysis & Machine Learning

Transform raw data into actionable insights through advanced analysis and machine learning techniques. With expertise in data exploration, regression, and classification, I can help your business uncover hidden trends, optimise operations, and implement scalable, results-driven solutions.

Process Optimisation & Engineering Design

Achieve energy efficiency, cost reduction, and sustainability goals with tailored engineering solutions. I specialise in multi-objective optimisation, delivering designs and process improvements that align with your unique operational needs and industry standards.

Portfolio

I am continuously expanding my skill set to address evolving challenges with agility and precision. I seamlessly adapt to new domains, integrate into ongoing projects, and consistently deliver impactful results. Below are highlights of my recent work in the industry, for additional project scopes please see my LinkedIn

Predictive Maintenance for Milling Equipment Using Classification Models

ChemDigital Ltd.

In this project, I tackled the problem of unplanned downtime in a diamond-tipped milling machine by developing a predictive maintenance pipeline. By analysing historical operational data, I employed a Random Forest-based classification model to predict tool-tip failures with high recall and precision. The project involved extensive data preprocessing, feature engineering, and hyperparameter tuning to deliver a robust predictive system for optimising asset life.

Key Achievements:

Reduced failure instances by over 25-fold compared to existing approach.
Improved tool replacement scheduling, minimising unnecessary replacements while preventing costly failures.
Demonstrated expertise in scaleable, repeatable machine learning pipeline development, feature engineering, and model evaluation.

View Report

Development of a Comprehensive Simulation Tool for Hydrogen and Derivatives

Wood Plc, Digitial Consulting

I led the transformation of a static, Excel-based tool into a scalable, user-friendly framework for evaluating hydrogen projects, renewable energy systems, ammonia production, and the use of batteries and mass storage as energy buffers. This comprehensive tool integrates CAPEX and OPEX calculations while supporting design optimisation, enabling detailed feasibility studies and providing actionable economic insights.

As technical lead, I directed the design, development, and interdisciplinary coordination of the project. I provided strategic guidance to the back-end and GUI development teams, ensuring their work aligned with technical requirements and stakeholder objectives.

The solution incorporates a Python-based simulation engine, an intuitive GUI for streamlined user interactions, and a collaborative back-end platform for efficient data sharing. This innovation significantly enhanced precision, efficiency, and accessibility, while also advancing design optimisation capabilities for hydrogen energy systems and renewable resource evaluations.

Key Achievements

Designed Scalable Tools: Developed a Python-based simulation engine tailored for feasibility studies, improving process evaluations.
Integrated Economic Insights: Embedded CAPEX and OPEX calculations for comprehensive viability analysis.
Directed Development: Guided the back-end and GUI teams for cohesive integration with project goals.
Streamlined Processes: Reduced feasibility study time, accelerating decision-making and reporting.
Secured Funding: Communicated updates effectively to gain additional project funding.

View Thesis

Universal Compartment Modelling Tool for Chemical Engineering

Industrial PhD

This research introduced CompArt, a universal compartment modelling tool for chemical engineering unit operations, which incorporates phenomenological models of multi/single phase: mass transfer, reactions, phase transport and convective flow. The project addressed the challenge of bespoke and error-prone model development in the field. CompArt combines a universal input language, an interpretation algorithm for generating ODE systems, and integration with advanced numerical solvers. By incorporating these complex models, it eliminates the need for specialised coding skills, allowing engineers to focus on model development rather than implementation. Validated against 20 benchmark models, this innovative tool significantly simplifies and standardises the compartment modelling process.

Key Achievements:

Developed a universal framework for building, solving, and validating compartment models.
Streamlined the simulation of complex chemical processes with stiff, non-linear dynamics.
Enhanced accessibility of advanced modelling techniques for broader applications.