Search

This work studies the development of a sustainable hydrogen infrastructure that supports the transition towards a low-carbon transport system in the United Kingdom (UK). The future hydrogen demand is forecasted over time using a logistic diffusion model, which reaches 50% of the market share by 2070. The problem is solved using an extension of SHIPMod, an optimisation-based framework that consists of a multi-period spatially-explicit mixed-integer linear programming (MILP) formulation. The optimisation model combines the infrastructure elements required throughout the different phases of the transition, namely economies of scale, road and pipeline transportation modes and carbon capture and storage (CCS) technologies, in order to minimise the present value of the total infrastructure cost using a discounted cash-flow analysis. The results show that the combination of all these elements in the mathematical formulation renders optimal solutions with the gradual infrastructure investments over time required for the transition towards a sustainable hydrogen economy. [ABSTRACT FROM AUTHOR]

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]

[Display omitted] The pharmaceutical industry is continuously overcoming ways to reduce its development times to market and bring new medicines to patients with the highest quality standards faster. This can be achieved with continuous manufacturing and digital design by minimising the amount of active pharmaceutical ingredient (API) needed in drug product design, early project de-risking, and reducing the use of clinical manufacturing equipment, rework, and quality investigations. This paper presents the digital twin of a continuous direct compression line combining first-principles models, residence time distribution (RTD) models obtained from discrete element method (DEM) simulations, science of scale tools and data-driven models from process data in a hybrid flowsheet approach. The flowsheet predicts critical process parameters in the feeders, blender, and tablet press, and critical quality attributes, like tablet composition, weight, thickness, and hardness. It allows the study of the steady state operation in the design space, the impact of operating conditions, material and process parameters, and the dynamic response to disturbances. This is used to de-risk and optimise drug product and process development while reducing the number of experiments. The digital twin also has the potential to guide manufacturing runs and respond to new drug product market approval queries using flowsheet modelling. [ABSTRACT FROM AUTHOR]

Batch scheduling and control decision levels are usually optimized separately, even though integrated decisions would potentially increase the overall performance of the plant. This work aims at including in the scheduling problem the operating conditions related to the control function, which are traditionally optimized and fixed beforehand, to shed light to the benefits that may be obtained when using variable recipes in contrast to fixed nominal recipes. A multiproduct and a multipurpose plant illustrate the proposed approach. In both cases, the potential benefits are confirmed resulting in clear advantages regarding overall economic benefit, when the variable recipes are introduced. Thus, the presented approach gives rise to new tradeoffs, since it allows new improvement opportunities associated with quality, safety, or changing operating policies, which are usually considered separately. [ABSTRACT FROM AUTHOR]

Abstract: The complexity of decision-making in process industries and the need of highly competitive organizations require new supporting tools to coordinate and optimize the information flow among decision levels. This work presents a framework for integrating the scheduling and control decision levels by means of an ontology, which allows and coordinates the information exchange among the different modeling paradigms/conventions currently used for the enterprise-wide optimization (EWO). The scheduling of two multiproduct batch plants with increasing complexity is presented for illustrating the proposed working procedure. [Copyright &y& Elsevier]