Biomethane can serve as an alternative to fossil fuels. Pipeline and compressor infrastructure must be built to enable the feed-in of biomethane from decentralized production sites into the existing gas network. Costs can be reduced by sharing this transport infrastructure among individual biogas plant operators. To find the cost-optimal layout, a mixed integer linear programming (MILP) problem is formulated. The model is expanded to show the effect of considering not only pipe cost but also compressor cost in the objective. It is applied to an Austrian region to find the optimal network for 14 biogas plants. After optimization, two allocation methods for sharing investment costs among biogas plants are explored: an equal cost approach and a volumetric share approach. Optimal network topology changes due to the modified objective, resulting in significant cost savings of 9 % for the total region and 19 % for a subregion. Investment costs for individual biogas plant operators vary widel... [more]

With the increasing share of volatile renewable energies, there is a growing need for flexible storage systems to balance fluctuations between generation and demand. Multi-energy systems, featuring battery and hydrogen storage systems, provide an efficient and scalable solution for this purpose. While rule-based control is primarily used in industry, model predictive control (MPC) is considered the most promising strategy for cost-optimized and safe operation. The performance of such controllers depends heavily on the capacities, power rates, and degradation behavior of the storage systems; for hydrogen systems, it can also depend on minimum switch-on/off times and ramping rates. This work aims to use sensitivity analysis to quantify the influence of variations in these component parameters on control quality. To this end, a lab-scale battery-hydrogen storage system (TU Wien) is modeled and operated using MPC to minimize grid exchange and thus increase self-sufficiency. Experimental da... [more]

Heat Pumps (HPs) can play a vital role in the decarbonization of heating in industry. The performance of a HP strongly depends on the refrigerant, the working fluid within the HP. In order to maximize HP performance, systematic selection of the refrigerant is key. Refrigerant choice affects the very feasibility of employing a HP to deliver heating to a process. A flexible and robust method is required to select refrigerants that are the best fit for a given heating application. A computer-aided molecular & process design (CAMPD) method is developed to design the optimal refrigerant that is tailored to process needs. The method is applied to three case studies across which the HP performance objectives and constraints, and heat source and heat sink temperatures are varied. In addition, the design of refrigerants with low (<150) global warming potentials and zero ozone depletion potentials is investigated. For all applications across all case studies, the CAMPD approach successfully iden... [more]

Modular plants emerged as the key solution for reducing time-to-market and increasing flexibility in the process industry by combining different modules known as Process Equipment Assemblies (PEAs). While PEA automation is standardised through the Module Type Package (MTP), comparable tools for their documentation remain absent. This work presents the Semantic PEA Datasheet (SPEAD) ontology, which represents PEA documentation as a machine-readable knowledge graph that adheres to the FAIR principles. SPEAD integrates established standards such as DEXPI and the VDI 2776 guidelines and ensures data quality through comprehensive annotations and constraint-based validation. The ontology was evaluated against twelve competency questions derived from a representative use case as well as competency questions from the literature using a continuous stirred-tank reactor PEA as well as a dosing PEA as example systems. SPEAD successfully covers operational and design parameters as well as interface... [more]

This talk presents possible strategies for the calibration of crystallization models aimed at predicting particle size distributions (PSDs) of active pharmaceutical ingredients (APIs) when using industrial datasets, which are limited in terms of number or information for the modeling exercise. Industrial data concerning a seeded batch cooling recrystallization of an API in an organic solvent are used as a case study, representing an example of the issues to be faced with real-world experimental datasets. The results are discussed showing how the model performances can be deemed satisfactory, at least from the industrial perspective, and how this can be useful to enhance process understanding and to guide process development and scale-up.