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Decarbonization of the power sector offers ammonia industry an opportunity to reduce its CO2 emissions through sector coupling. Extending from our previous work, we propose a power‐based process for ammonia and nitric acid production. The coupling of nitric acid production facilitates highly efficient heat integration between steam electrolysis and the rest of the process. We investigate the economic performance of the production complex through a model‐based dynamic optimization approach, considering scenarios with or without incorporation of intermittent wind power as well as deployment of battery storage. In all cases, the wind power integration proves to be economic with a peak‐to‐base load ratio of up to 2.3. The new process reduces primary energy consumption by more than 13% compared to conventional technologies. However, it is only economically competitive with help of either a low‐cost battery storage or a higher carbon price on fossil fuels. The results also confirm the importance of considering process dynamics during process design. [ABSTRACT FROM AUTHOR]

The adaptive switching structure approach is generalized from single-stage problems and single-shooting to multi-stage problems and multiple-shooting. This generalization is based on previous work on the exploitation and detection of the control switching structure and wavelet-based control grid refinement for single-stage problems. Here, single-shooting is employed to transcribe the multi-stage optimal control problem (OCP) into a nonlinear programming problem. The proposed multi-stage formulation is also capable to represent the transcription of single-stage OCP stemming from multiple-shooting. Thus, the previously reported adaptive multiple-shooting approach is extended by an adaptation of the switching structure. Finally, a new stopping criterion is introduced that measures the intermediate constraint violation at the optimal solution. The proposed adaptive switching structure detection is illustrated for a multi-stage and a multiple-shooting problem using the Williams–Otto semi-batch reactor. A solution of user-specified accuracy in the objective and the path-constraints can be obtained using only few decision variables. [ABSTRACT FROM AUTHOR]

Highlights: [•] Wavelet-based grid is applied to direct multiple shooting. [•] Adaptive multiple shooting is used to solve dynamic optimization (or optimal control) problems. [•] Adaptive multiple shooting, automatically generates a problem-dependent parameterization of the control profiles. [•] We demonstrate the superiority of adaptive grid refinement over an equidistant discretization for two examples. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we present a novel method for the numerical solution of dynamic optimization problems. After obtaining a first solution at a coarse resolution of the control profiles with a direct sequential approach, the structure of the control profiles is analyzed for possible switching times and arcs. Subsequently, the problem is reformulated automatically and solved as a multi-stage problem, with each stage corresponding to a potential arc. Order and resolution of the control parameterization are adapted to the type of the particular arc. By means of some case studies we show that accurate solutions with only few degrees of freedom can be obtained. [Copyright &y& Elsevier]

In this work we present an approach for the sensitivity analysis of linearly-implicit differential–algebraic equation systems. Solutions for both states and sensitivities are obtained by applying an extrapolated linearly implicit Euler discretization scheme. This approach is compared to the widely used sensitivity extensions of multi-step BDF methods by means of case studies. Especially, we point out the benefit of this method in the context of dynamic optimization using the sequential approach. [Copyright &y& Elsevier]

This paper introduces an optimization-based approach for the simultaneous solution of batch process synthesis and plant allocation, with decisions like the selection of chemicals, process stages, task-unit assignments, operating modes, and optimal control profiles, among others. The modeling strategy is based on the representation of structural alternatives in a state-equipment network (SEN) and its formulation as a mixed-logic dynamic optimization (MLDO) problem. Particularly, the disjunctive multistage modeling strategy by Oldenburg and Marquardt (2008) is extended to combine and organize single-stage and multistage models for representing the sequence of continuous and batch units in each structural alternative and for synchronizing dynamic profiles in input and output operations with material transference. Two numerical examples illustrate the application of the proposed methodology, showing the enhancement of the adaptability potential of batch plants and the improvement of global process performance thanks to the quantification of interactions between process synthesis and plant allocation decisions. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we present a method for the optimization of dynamic systems using problem-adapted discretizations. The method is based on the direct sequential or single-shooting approach, where the optimization problem is converted into a nonlinear programming problem by parameterization of the control profiles. A fully adaptive, problem-dependent parameterization is generated by repetitive solution of increasingly refined finite-dimensional optimization problems. In each step of the proposed algorithm, the adaptation is based on a wavelet analysis of the solution profiles obtained in the previous step. The method is applied to several case study problems to demonstrate that the adaptive parameterization is more efficient and robust compared to a uniform parameterization of comparable accuracy. [Copyright &y& Elsevier]