SPICE: A Computer-Aided Framework for Systematic Process Intensification of Chemical Enterprises

Process intensification facilitates the improvement of process performance and leads to substantially smaller, cleaner, safer, and more energy-efficient technologies [1-2]. Several methods (e.g., [3-6]) and computer-aided tools and software frameworks (e.g., [7-11]) exist for the intensification of chemical processes. In this work, we describe a computational framework SPICE which stands for Systematic Process Intensification of Chemical Enterprises. SPICE identifies optimal intensified process designs for user-specified raw materials, product specifications, chemical species, plausible chemistry (reactions), and enabling materials for separation, conversion and/or storage. We have recently used a new superstructure using fundamental building blocks for the systematic design and intensification of chemical processes [6]. A mixed-integer integer nonlinear optimization (MINLP) model describing the building block superstructure has been also developed. This MINLP model constitutes the core of SPICE. Specifically, the framework (i) contains the interface for collecting the model input data on feeds, products and materials, (ii) generates optimal block configurations by solving the MINLP model to optimality using a commercial solver, and (iii) converts the resultant block configurations into classical process flow diagrams. A key feature of SPICE is that the users do not need to specify any superstructure a priori. The MINLP-based model incorporates general mass and energy conservations, and descriptions of reaction, separation and material selection towards designing an intensified chemical process. SPICE also provides options for incorporating new reaction models and materials properties. Additional constraints can be also set on product demands and waste generation to meet certain industrial and/or environmental specifications.

Using several intensification examples, we will demonstrate the capabilities of the software framework in effective collection and transferring of input data through a user-friendly interface, and automatic generation of process flowsheet. In addition, we will discuss a systematic method, which has been implemented in SPICE, to automatize the conversion of building block-based diagrams to classical process flow diagrams and vice-versa. This method allows to systematically identify the primary and auxiliary units from the corresponding solutions of the building-block based MINLP model. Evaluation on flowrate variables between blocks gives the information on connectivity for construction of an adjacency matrix with nodes as identified units and edges as connectivity [12]. This adjacency matrix is used to interpret the connectivity within a process flowsheet.

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