In this work, a novel systematic techno‐economic analysis framework is proposed for costing intensified modular systems. Conventional costing techniques are extended to allow estimation of capital and operating costs of modular units. Economy of learning concepts are included to consider the effect of experience curves on purchase costs. Profitability measures are scaled with respect to production of a chemical of interest for comparison with plants of traditional scale. In the developed framework, a base case scenario is analyzed to identify the relevance of the economy of learning and cost parameters that are yet to be established for modular projects that will be deployed. Then, a sensitivity analysis step is conducted to define changes in relevant variables that benefit the construction of modular systems. In a final step, scenarios in which the modular technology presents break‐even and further reduction in cost are identified. A process model for a modular hydrogen unit is developed and used for demonstration of the proposed framework. In this application, process synthesis is carried out, including operability analysis for selection of feasible operating conditions. A comparison with a benchmark conventional steam methane reforming plant shows that the modular hydrogen unit can benefit from the economy of learning. A synthesized flowsheet for a modular steam methane reforming plant is used to map the decrease in natural gas price that must be needed for the plant to break even when compared to traditional technologies. Scenarios in which the natural gas price is low allow break‐even cost for both individual hydrogen units and the assembled modular plant. For such break‐even cases, the economy of learning must produce a reduction of 40% or less in capital cost when the natural gas price is under 0.02 US$/Sm3. This result suggests that the synthesized modular hydrogen process has potential to be economically feasible under these conditions. The developed tools can thus be used to accelerate the deployment and manufacturing of standardized modular energy systems.

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Self-optimizing control technologies are a well-known study field of control structure design, having a robust mathematical background. With the aid of commercial process simulators and numerical packages, process modeling became an easier task. However, dealing with extremely large and complex systems still is a tedious task, and sometimes not feasible, even with these innovative tools. Surrogate models, also called metamodels, can be used to substitute partially or totally the original mathematical models for prediction and optimization purposes, reducing the complexity of evaluating large-scale and highly nonlinear processes. This work aims at applying recent self-optimizing control techniques to surface responses of processes using the Kriging method as a reduced model builder. A procedure to apply self-optimizing control to surrogate responses was described in detail, together with how the optimization can be done. Well-known case studies had their surface responses successfully built and analyzed to generate using the techniques cited, the optimal selection of controlled variables that minimizes the worst-case loss, and the same results were found when compared with the implementation in the original models from previous authors. The results indicate the effectiveness of the reduced models when applied to design self-optimizing control structures, simplifying the task.

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The objective in this work is to propose a novel approach for solving inverse problems from the output space to the input space using automatic differentiation coupled with the implicit function theorem and a path integration scheme. A common way of solving inverse problems in process systems engineering (PSE) and in science, technology, engineering and mathematics (STEM) in general is using nonlinear programming (NLP) tools, which may become computationally expensive when both the underlying process model complexity and dimensionality increase. The proposed approach takes advantage of recent advances in robust automatic differentiation packages to calculate the input space region by integration of governing differential equations of a given process. Such calculations are performed based on an initial starting point from the output space and are capable of maintaining accuracy and reducing computational time when compared to using NLP‐based approaches to obtain the inverse mapping. Two nonlinear case studies, namely a continuous stirred tank reactor (CSTR) and a membrane reactor for conversion of natural gas to value‐added chemicals are addressed using the proposed approach and compared against: (i) extensive (brute‐force) search for forward mapping and (ii) using NLP solvers for obtaining the inverse mapping. The obtained results show that the novel approach is in agreement with the typical approaches, while computational time and complexity are considerably reduced, indicating that a new direction for solving inverse problems is developed in this work.