gPROMS is a platform for high-fidelity predictive modelling for the process industries. Its main applications are in model-based engineering activities for process and equipment development and design, and optimisation of process operations. gPROMS is applied by major process and technology organisations throughout the world, across many application areas in all process sectors. It is also used for research and teaching at 200 academic institutions worldwide. At the heart of the gPROMS platform is the gPROMS ModelBuilder, the leading custom modelling environment for the process industries. ModelBuilder's process modelling, process simulation and optimisation capabilities are used to generate high-accuracy predictive information for decision support in product and process innovation, design and operation. gPROMS platform products have many major advantages over other comparable modelling software: * Support for multiscale modelling, meaning - for example - that it is possible to create a reactor model that takes into account all phenomena from mass transfer in the catalyst pore to full-scale equipment effects simultaneously * Custom modelling capabilities that allow the development, execution and maintenance of high-fidelity models of a wide range of equipment * The abilty to apply such high-fidelity unit models within a full process flowsheet * Estimation of equipment or process empirical parameters from experimental - laboratory, pilot or operational - data, with estimates of data uncertainty for risk analysis * Steady-state and dynamic modelling within the same environment * The ability to perform many different activities using the same underlying model * Support for scale-up from minimal experimental data to full equipment, using hybrid modelling technologies where necessary. gPROMS is an equation-oriented modelling system used for building, validating and executing first-principles models within a flowsheeting framework. Models are constructed in the gPROMS ModelBuilder by writing down the fundamental chemistry, physics, chemical engineering, operating procedures and other relationships that govern the process or product behaviour. The resulting model is then validated against observed data - typically, laboratory, pilot plant or operating data - to adjust model parameters such as heat transfer coefficients to match reality as closely as possible. Of course, it is not necessary to create a model from scratch every time - you can use one of the many state-of-the-art gPROMS model libraries, or create your own library for publishing throughout your organisation. Once a model exists, it can be solved in many different ways to perform many different activities - for example: * Steady-state simulation, for example to optimise steady-state process conditions * Dynamic simulation, for example in control design or design of operating procedures * Parameter estimation, to determine empirical parameters from real-life data * Model-based experiment design, to design the optimal next experiment to generate additional data in areas of uncertainty * Steady-state and dynamic optimisation, including integer optimisation, for direct calculation (as opposed to trial-and-error simulation) of variables or trajectories of interest * Generation of linearised models for use in control and online optimisation The activities can be applied at different stages across the process lifecycle. This means that once you have invested in creating an accurate gPROMS model of your process you can use that model wherever it can generate value, to ensure multiple return on investment.