Historically, there has been an over-reliance on experimentation and trial and error as a means of process development and scale-up. Given the impact on development timescales and costs, both manufacturers and regulators are seeking to adopt better solutions in this area.
DynoChem is the first and only software tool that genuinely supports the development of process understanding at both a project and wider organization level. We provide a framework in which both chemists and engineers can apply the scientific method, using modeling and simulation alongside experiments, developing more robust processes quickly and achieving quality by design (QbD). That understanding can be shared easily with colleagues in development, the pilot plant, technology transfer, technical operations and manufacturing.
A fully functional trial copy of DynoChem is available on request.
Reduce number of experiments, amount of time and material required for reaction development and optimisation. Assess and characterize equipment; look up physical properties; identify steps that have safety issues, are yield-limiting or impurity-forming, build predictive models, determine acceptable operating window / design space; anticipate and optimise process robustness on scale; take account of both chemical and equipment-related factors.
Create models directly from your data, get a feel for reaction profiles and effects of equivalents and temperature. Fit parameters, distinguish reaction mechanisms and find optimum conditions quickly.
Predict safe feed times for dosing-controlled reactions from Qr profiles and gas evolution rates; find conditions to make reactions dosing-controlled; leverage process development insight from Qr and IR data; include DynoChem report as appendix in safety report.
Explore and map factor space using a mechanistic model alongside statistical DOE tools. Take account of model uncertainty and lack of fit when overlapping responses and defining design spaces. Suggest where additional experiments would reduce uncertainty. Select factors for studies so that design space will be scalable.
Perform process fit, heat transfer and agitation calculations; determine distillation pressures, temperatures, solvent requirements and times; reduce drying times; use models from earlier development for smooth technology transfer.
Anticipate 'spoilers' due to equipment limitations; use lab data to assist with validation, troubleshooting and optimisation projects.
Regress solubility data; assess vessels and mixing; simulate all types of crystallisation; fit growth kinetics to concentration data; predict and control supersaturation profiles; interpret and model size distribution data.