This work focuses on linear and non-linear programming techniques for feasibility assessment, synthesis, and optimization of process networks. Analytical techniques, dimensionality reduction, convexification strategies, and functional analysis are developed to identify global optima and to enhance the efficiency of their computation. Green engineering, especially the hydrogen economy, is the primary motivation of this work. To this end, these strategies are implemented in identification of the attainable region for separator networks, optimization of compressors and coolers in series delivering hydrogen at high pressure, and minimum utility cost of an energetically-enhanced steam methane reforming process in the presence of carbon tax legislation.
Pursuant to feasibility assessment of systems with respect to delivery of pure compounds, the applicability of the Infinite DimEnsionAl State-space (IDEAS) framework is extended to attainable region (AR) identification for separator networks. The AR for a water/methanol/acetone mixture involving one network feed stream with known species molar fractions and three network outlet streams at 1 atm pressure. The binary methanol/acetone system exhibits a minimum-boiling azeotrope at 79.07% mole fraction of acetone at 328.5 K. The IDEAS-generated AR successfully identified that the binary methanol/acetone azeotrope was inside the AR, and thus demonstrated there exists a separator network that can bypass the azeotrope.
In the exploration of hydrogen as an alternative energy source, the minimum operating cost and minimum capital cost problems for a system of compressors and coolers in series that bring a gas with constant compressibility factor from a specified initial state (T0, P0) to a specified final state (T0, Pn). Through mathematical proof, the dimensionality of the optimization problems is reduced and analytical properties of the compressor outlet temperatures, when either operating costs or capital costs dominate, are established. A case study involving hydrogen compression was done to illustrate the methods, and shows the global optimum achieves cost savings of up to 13% from conventional designs.
Lastly, a novel method for solution of linear parametric programming problems is proposed based on the concept of dimensionality reduction – this allows the analytic quantification of the optimum objective function value and associated optimum variable vector. Regions in carbon/renewable utility cost coefficient ratio space are identified, in which one technology is superior over the other. EESMR is shown to be preferable in the presence of significant levels of taxation on the use of natural gas as fuel.
