DynoChem's features make it the natural choice for scientists and engineers working in chemical development and scale-up. That's no accident - we designed it specifically for the challenges that you face. Compare this list of features with those of any other vendor:
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.