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We describe a novel solar-based process for the production of methanol from carbon dioxide and water. The system utilizes concentrated solar energy in a thermochemical reactor to reenergize CO 2 into CO and then water gas shift (WGS) to produce syngas (a mixture of CO and H 2 ) to feed a methanol synthesis reactor. Aside from the thermochemical reactor, which is currently under development, the full system is based on well-established industrial processes and component designs. This work presents an initial assessment of energy efficiency and economic feasibility of this baseline configuration for an industrial-scale methanol plant. Using detailed sensitivity calculations, we determined that a breakeven price of the methanol produced using this approach would be 1.22 USD/kg; which while higher than current market prices is comparable to other renewable-resource-based alternatives. We also determined that if solar power is the sole primary energy source, then an overall process energy efficiency (solar-to-fuel) of 7.1% could be achieved, assuming the solar collector, solar thermochemical reactor sub-system operates at 20% sunlight to chemical energy efficiency. This 7.1% system efficiency is significantly higher than can currently be achieved with photosynthesis-based processes, and illustrates the potential for solar thermochemical based strategies to overcome the resource limitations that arise for low-efficiency approaches. Importantly, the analysis here identifies the primary economic drivers as the high capital investment associated with the solar concentrator/reactor sub-system, and the high utility consumption for CO/CO 2 separation. The solar concentrator/reactor sub-system accounts for more than 90% of the capital expenditure. A life cycle assessment verifies the opportunity for significant improvements over the conventional process for manufacturing methanol from natural gas in global warming potential, acidification potential and non-renewable primary energy requirement provided balance of plant utilities for the solar thermal process are also from renewable (solar) resources. The analysis indicates that a solar-thermochemical pathway to fuels has significant potential, and points towards future research opportunities to increase efficiency, reduce balance of plant utilities, and reduce cost from this baseline. Particularly, it is evident that there is much room for improvement in the development of a less expensive solar concentrator/reactor sub-system; an opportunity that will benefit from the increasing deployment of concentrated solar power (electricity). In addition, significant advances are achievable through improved separations, combined CO 2 and H 2 O splitting, different end products, and greater process integration and distribution. The baseline investigation here establishes a methodology for identifying opportunities, comparison, and assessment of impact on the efficiency, lifecycle impact, and economics for advanced system designs.

in Wiley Online Library (wileyonlinelibrary.com).In principle, optimization-based ''superstructure'' methods for process synthesis can be more powerful than sequential-conceptual methods as they account for all complex interactions between design decisions. However, these methods have not been widely adopted because they lead to mixed-integer nonlinear programs that are hard to solve, especially when realistic unit operation models are used. To address this challenge, we develop a superstructure-based strategy where complex unit models are replaced with surrogate models built from data generated via commercial process simulators. In developing this strategy, we study aspects such as the systematic design of process unit surrogate models, the generation of simulation data, the selection of the surrogate's structure, and the required model fitting. We also present how these models can be reformulated and incorporated into mathematical programming superstructure formulations. Finally, we discuss the application of the proposed strategy to a number of applications.

In this study, we first develop an integrated strategy for the catalytic conversion of lignocellulose into liquid fuels based on the production of levulinic acid (LA) followed by its hydrogenation to gvalerolactone (GVL). Our integrated strategy involves a novel catalytic conversion process employing alkylphenol-based separation to extract LA from the sulfuric acid containing aqueous solution following the sulfuric acid catalyzed deconstruction of cellulose. To minimize utility consumption, we perform heat integration, while the remaining heating requirement is satisfied by the combustion of residual biomass. Hot combustion gases are also used to generate electricity, which meets the electricity requirement of the process, while the excess electricity is sold to the grid. We perform a technoeconomic analysis for the alkylphenol-based strategy and compare its economics with a previously reported strategy, in which butyl acetate was used as an extractive solvent to separate GVL from sulfuric acid following the LA hydrogenation step. With some improvements in the process configuration, the alkylphenol strategy leads to a minimum selling price (MSP) of $4.40 per gallon of gasoline equivalent (GGE) for compatible biofuel components, whereas the butyl acetate strategy leads to a MSP of $4.68 per GGE. We show that the alkylphenol strategy becomes a significantly better choice when the catalyst lifetime for the hydrogenation of LA becomes less than 6 months.

We present a framework for the efficient representation, generation, and modeling of superstructures for process synthesis. First, we develop a new representation based on three basic elements: units, ports, and conditioning streams. Second, we present four rules based on "minimal" and "feasible" component sets for the generation of simple superstructures containing all feasible embedded processes.Third, in terms of modeling, we develop a modular approach, and formulate models for each basic element. We also present a canonical form of element models using input/output variables and constrained/free variables. The proposed methods provide a coherent framework for superstructure-based process synthesis, allowing efficient model generation and modification.