Website:
paragongreentech.com
Job details:
Company and Engagement
Paragon Greentech is an emerging organisation focused on sustainable technologies, process optimisation and data-driven industrial improvement.
Position: Process Simulation Contractor – DWSIM Specialist
Type: Independent contractor / freelance / project-based
Location: Primarily remote, with online technical meetings and possible site visits.
Project Overview
We are improving a proprietary and differentiated manufacturing process involving novel process-engineering concepts.
We seek an experienced DWSIM specialist to develop, validate, document and progressively expand process simulation models. The immediate requirement is to simulate the latest proposed innovation. Subject to successful completion, the engagement may extend to a dynamic model of the complete process within defined battery limits and later AI integration.
The long-term objective is a continuously usable model supporting improvements in safety, quality, yield, throughput, resource efficiency, reliability, cost and returns.
Existing Engineering Information
A substantial amount of work on the base process is already complete. The contractor will not be expected to recreate it from first principles.
After execution of the NDA, available information may include process descriptions, design basis, PFDs, P&IDs, detailed engineering documents, equipment data, balances, stream and utility data, control information, calculations, operating procedures, layouts, vendor information, test data, operating data and previous studies.
The contractor must review, reconcile and translate this material into maintainable DWSIM models. Gaps, conflicts and assumptions must be documented, with key inputs traceable to source documents.
Phased Scope
The engagement is expected to proceed in three phases. Later phases will depend on performance, feasibility, data availability and agreed terms. Phase 1 must support future expansion.
Phase 1 – Latest Proposed Innovation
Develop and validate a DWSIM simulation of the latest proposed innovation within the existing process.
Responsibilities include:
- Review the engineering package and establish the agreed baseline and applicable document revisions.
- Build or modify the DWSIM model, including components, reactions, kinetics, phase behaviour, streams, recycles, utilities, constraints and equipment duties.
- Select and justify thermodynamic and property methods.
- Model relevant standard or custom equipment.
- Compare the innovation with the current process or agreed baseline.
- Assess its effect on yield, quality, throughput, raw materials, energy, utilities, waste, emissions, cost, operability, maintenance, safety and returns.
- Perform sensitivity and scenario analyses.
- Identify bottlenecks, control challenges, scale-up issues, risks, data gaps and potential improvements.
- Apply independent engineering judgement rather than merely reproducing supplied information.
Deliverables:
- Functional, editable DWSIM model and supporting files.
- Baseline process note, document register and source-traceability register.
- PFD, stream tables, material and energy balances, equipment-duty and utility summaries.
- Basis of design, assumptions register and thermodynamic/reaction-model justification.
- Baseline comparison, sensitivity studies and scenario analysis.
- Engineering-review log, data-gap register, limitations, technical-risk assessment and recommendations.
- User notes for operating and modifying the model.
Phase 2 – Dynamic Model of the Complete Process
Develop a dynamic model of the full process within agreed battery limits for day-to-day operational and engineering decision support.
The model should support optimisation of safety, quality, yield, throughput, resources, equipment utilisation, scheduling, reliability, cost and returns.
It should consider changing demand, supply constraints, feed composition and quality, raw-material and utility prices, equipment availability, plant and laboratory data, product quality, schedules, inventories, maintenance needs and safety, environmental and commercial limits.
Responsibilities and deliverables include:
- Define process, utility, storage, recycle, waste and support-system boundaries.
- Model units, storage, transfers, controls, operating logic, delays, constraints and interdependencies.
- Model start-up, shutdown, stoppages, feed or product changes, disturbances, trips, abnormal conditions and recovery.
- Establish structured methods to import historical, current and forecast data.
- Map plant tags and data sources to model variables.
- Develop reconciliation, calibration, validation and recalibration procedures.
- Develop operating-envelope, bottleneck, constraint, scenario and optimisation tools.
- Deliver the editable dynamic model, validation report, scenario library, operating manual, maintenance procedure, training and handover.
The model must distinguish measured, estimated, assumed, calculated and predicted values.
Phase 3 – AI Enablement and Integration
Prepare the model, data structure and workflows for integration with AI tools or agents.
Potential applications include deviation detection, quality and yield prediction, root-cause analysis, demand and supply forecasting, process optimisation, predictive maintenance, recommended set-points, automated scenarios, reporting and natural-language interaction.
Responsibilities include:
- Make model inputs, outputs, parameters, constraints and scenarios programmatically accessible where feasible.
- Establish machine-readable naming, tags, units, limits, validation rules and a data dictionary.
- Define APIs, scripts, connectors, databases, middleware and data-exchange methods.
- Assist with AI-use-case selection and integration architecture.
- Define human approvals, safeguards, access controls, audit trails, version control and cybersecurity requirements.
- Ensure recommendations remain within safety, equipment, quality, environmental, regulatory and commercial limits.
- Develop and test agreed proof-of-concept use cases.
Deliverables may include an AI-readiness assessment, integration architecture, data dictionary, interface specifications, governance and safeguard framework, proof of concept, validation report and roadmap.
General Model Requirements
Models must be modular, transparent, documented, version-controlled and maintainable, with native editable files and dependencies handed over.
Required Experience
Applicants should demonstrate strong capability in:
- DWSIM and chemical-process simulation.
- Steady-state and dynamic simulation.
- Material and energy balances.
- Thermodynamics, heat and mass transfer, fluid flow and reaction engineering.
- Process design, optimisation, equipment modelling and scale-up.
- Process safety, data analysis, model validation and technical documentation.
Experience with other process simulators, Python, MATLAB, APIs, plant-data systems, digital twins, machine learning, AI agents or industrial automation is advantageous.
A relevant engineering degree and five or more years of suitable experience are preferred. Exceptional candidates with less experience may also be considered.
Working Style
The ideal contractor will act as an engineering partner, challenge assumptions constructively, document decisions, communicate clearly and meet deadlines.
Confidentiality, AI Use and Intellectual Property
The selected contractor must execute an NDA before receiving confidential information.
Confidential information may be used only for this engagement and may not be disclosed, uploaded to unapproved systems or entered into public or third-party AI tools without written approval. Any subcontractor, cloud platform, external consultant or AI tool must be approved in advance.
All project-specific models, calculations, diagrams, code, scripts, spreadsheets, connectors, documentation, optimisation routines, improvements and related work products will belong to the client, subject to the final contract and payment terms. Any pre-existing or third-party intellectual property must be disclosed and approved.
Proposal Requirements
Applicants should provide:
- CV, qualifications and summary of relevant DWSIM experience.
- Examples of comparable work, subject to confidentiality.
- Dynamic-simulation, plant-data, API, digital-twin or AI-integration experience.
- Proposed approach to Phase 1.
- Availability, proposed timeline and commercial terms.
- References and conflicts of interest.
Applicants should briefly describe their most complex DWSIM assignment, dynamic-modelling experience, approach to thermodynamic selection, incomplete data and model validation, experience with custom unit operations, scripts, APIs, plant data or AI; and how Phase 1 would be structured for later expansion. The immediate appointment is expected to cover Phase 1. Successful completion may lead to Phases 2 and 3, ongoing model maintenance and further process-engineering support. No commitment to later phases is implied unless agreed in writing.
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