Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet simulations with constraints

Flowsheet simulations of chemical processes on an industrial scale require the solution of large systems of nonlinear equations,so that solvability becomes a practical issue.Additional constraints from technical,economic,environmental,and safety considerations may further limit the feasible solution space beyond the convergence requirement.A priori,the design variable domains for which a simulation converges and fulfills the imposed constraints are usually unknown and it can become very time-consuming to distinguish feasible from infeasible design variable choices by simply running the simulation for each choice.To support the exploration of the design variable space for such scenarios,an adaptive sampling technique based on machine learning models has recently been proposed.However,that approach only considers the exploration of the convergent domain and ignores additional constraints.In this paper,we present an improvement which particularly takes the fulfillment of constraints into account.We successfully apply the proposed algorithm to a toy example in up to 20 dimensions and to an industrially relevant flowsheet simulation.

Overview of clinical forensic services in various countries of the European Union

Examination of a person who has been a victim of a physical or sexual assault may be very important for upcoming legal proceedings.In the context of a clinical forensic examination,physical findings are recorded and biological trace material is gathered and secured.Ideally,all forensic findings are documented in a detailed report combined with photographic docu-mentation,which employs a forensic scale to depict the size of the injuries.However,the integrity of such forensic findings depends particularly on two factors.First,the examination needs to be conducted professionally to ensure that the findings are properly admissible as court evidence.Second,the examination should take place as soon as possible because the opportunity to successfully secure biological samples declines rapidly with time.Access to low-threshold clinical forensic examinations is not evenly provided in all member states of the European Union(EU);in some states,they are not available at all.As part of the JUST_(e)U!(Juridical standards for clinical forensic examinations of victims of violence in Europe)pro-ject,the Ludwig Boltzmann Institute for Clinical Forensic Imaging in Graz,Austria created(in cooperation with its international partner consortium)a questionnaire:the purpose was to collect information about support for victims of physical and/or sexual assault in obtaining a low-threshold clinical forensic examination in various countries of the EU.Our paper pro-vides a summary of the responses and an overview of the current situation concerning pro-vided clinical forensic services.

Decision support for the development,simulation and optimization of dynamic process models

Simulation is besides experimentation the major method for designing,analyzing and optimizing chemical processes.The ability of simulations to reflect real process behavior strongly depends on model quality.Validation and adaption of process models are usually based on available plant data.Using such a model in various simulation and optimization studies can support the process designer in his task.Beneath steady state models there is also a growing demand for dynamic models either to adapt faster to changing conditions or to reflect batch operation.In this contribution challenges of extending an existing decision support framework for steady state models to dynamic models will be discussed and the resulting opportunities will be demonstrated for distillation and reactor examples.