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With the increasing concern for climate change, renewable and sustainable energy production has attracted considerable attention from the scientific community, industrial practitioners, and policy and decision-makers. There are many technological alternatives for each sub-category of complex sustainable energy systems. Life cycle assessment (LCA) can be an effective tool to compare the environmental impacts of each pathway and identify the most promising alternatives from an environmental impact perspective. This contribution first reviews the environmental assessment methods and tools developed over the years. Secondly, a comprehensive review of the contribution of the PSE community to the environmental impact analysis of renewable energy systems is performed. It is observed that while LCA is the preferred method, these studies differed widely concerning the choice of impact assessment method used, the level of details shared concerning the underlying LCA calculations, and whether or not sensitivity and uncertainty analyses were carried out, among many others. This makes the comparison of results from different studies difficult and often impossible. It is clear that the PSE community, with its emphasis on systems thinking and holistic approaches, plays a critical role in the design, integration, and operation of complex sustainable energy systems. However, the thorough calculations necessary to ensure a robust and transparent LCA analysis require a shared methodology and a detailed description of the rules. Such explicit, systematic, and transparent methods will set the bar for a minimum requirement for thorough LCA calculations, ensuring fair comparison and discussions of different technical solutions developed in the wider PSE community for sustainable renewables.

The use of fluctuating renewable energy over a certain threshold may lead to an unmanageable mismatch between the electricity generation and demand profiles threatening the grid's stability. In this study, an innovative complex energy storage/conversion system is proposed for the cogeneration of electricity, cooling, and water by integrating the liquefied natural gas (LNG) regasification process, an organic Rankine cycle, a compressed air energy storage (CAES) system, and a multi-effect distillation unit. The study attempts to minimize the CO2 emission from the CAES technology while addressing interruptions and reductions in the grid upon the extensive use of intermittent renewables. In addition, the proposed system uses excess power and waste heat during the charging and discharging of the CAES to regasify LNG and produce fresh water. The reference system performance is analyzed considering thermodynamic, economic, and environmental perspectives. The multi-objective grasshopper optimization algorithm is used to make a trade-off between the technical, economic, and environmental performance factors of the system. The results show an exergy efficiency of 50.6 % and a total cost rate of 322.8 $/h for the proposed system at the TOPSIS optimal point. The Grassmann diagram indicates the combustion chamber is the main source of irreversibility, and the Chord diagram revealed the discharge unit was responsible for more than 55 % of the total cost.

Global sensitivity analysis found a widespread use in the modeling community to study the input and output relationship of typically complex numerical models. Many sensitivity analysis studies have been published over the years across different domains, from considering simple test problems to more complex case studies involving large numerical models. However, very few studies have addressed the issue of the presence of fat-tailed distributions and its implication for the sensitivity analysis. First, we recall how the law of large numbers slowly convergences depending on the extent of tails in the distributions. Then, we present some methods to study Paretianity in the data and estimate the tail index. We then apply these concepts to a real- world global sensitivity problem using a case study of long- term measurements of N2O emissions dataset from WWTPs. We then propose a robust sensitivity metric based on mean absolute deviation for parameter importance ranking under fat- tailed distributions.

Denmark has been extracting gas and oil from the North Sea since the 1970s. However, Denmark has recently committed to phasing out fossil fuel production by 2050 to meet the climate goals of the Paris Agreement. The decommissioning of the offshore infrastructure is going to be very expensive. Therefore, considering effective ways for its repurposing becomes of great interest. In parallel to this scenario, the energy transition towards renewable and sustainable energy supply has been boosting the construction of offshore wind farms (OWF) in the North Sea. In this context, the integration of OWF with offshore oil and gas (O&G) platforms could result in a better alternative to decommissioning. In this work, a novel zero-carbon emission energy system for both power generation and methane production is proposed. By utilizing surplus electricity from OWF, electrolysis can be used to split water into oxygen and hydrogen, which can be used in the Allam cycle for power generation and methanation according to the Sabatier reaction, respectively. In this novel integrated system, surplus electricity from wind farms, seawater, and CO2 are converted into controllable electricity, methane, and oxygen. The synthesized methane can be partly stored or exported via existing natural gas pipelines. The portion to be stored/exported is defined as the storage ratio in this study. The integrated system has high flexibility since the Allam cycle and methanation unit can be operated separately or simultaneously. To validate the feasibility of the system, preliminary energy and exergy analysis are performed using Engineering Equation Solver (EES) software. 1% (45 ton/day) of the total daily Danish natural gas consumption is assumed as the baseline for the modeling. If all the synthesized methane is burned in the Allam cycle, 51.34 MW of electricity from wind farms can be transformed into 16.4 MW of controllable electricity. In this scenario, the integrated system can be regarde

Quantitative structure–property relationships (QSPRs) are important tools to facilitate and accelerate the discovery of compounds with desired properties. While many QSPRs have been developed, they are associated with various shortcomings such as a lack of generalizability and modest accuracy. Albeit various machine-learning and deep-learning techniques have been integrated into such models, another shortcoming has emerged in the form of a lack of transparency and interpretability of such models. In this work, two interpretable graph neural network (GNN) models (attentive group-contribution (AGC) and group-contribution-based graph attention (GroupGAT)) are developed by integrating fundamentals using the concept of group contributions (GC). The interpretability consists of highlighting the substructure with the highest attention weights in the latent representation of the molecules using the attention mechanism. The proposed models showcased better performance compared to classical group-contribution models, as well as against various other GNN models describing the aqueous solubility, melting point, and enthalpies of formation, combustion, and fusion of organic compounds. The insights provided are consistent with insights obtained from the semiempirical GC models confirming that the proposed framework allows highlighting the important substructures of the molecules for a specific property.

Rumor has it that all technologies needed to build energy islands are ready. Wind turbines are spinning in many large offshore parks, while combinations of sand and concrete have given birth to several entirely new islands. However, not all rumors are true. Not only has the Danish parliament mandated the largest ever infrastructure project in the history of our country. The first Danish artificial island built for energy production will also become the world’s largest renewable energy project. On top of the technical and logistical challenges associated with building something of an unprecedented scale and nature come new concerns. The energy islands are an extreme version of the power system we know today, and therefore represent a Mars mission for the energy system. More than once have large infrastructure projects been plagued by delays and significant additional costs. Often such problems have been rooted in overly optimistic planning, limited knowledge regarding the complexity and interdependencies involved, and not giving enough attention to the development phase relative to the construction phase. For many reasons, it is highly desirable for the energy island projects to perform well. Therefore, we have teamed up to map the key challenges and suggest R&D initiatives to address them. Importantly, these initiatives are not intended as an inserted step before construction. Given the urgency in green transition and ending the reliance on fossil fuels, research and construction must be conducted in parallel. A solid foundation for energy islands On the following pages we invite you to delve into the complexity of constructing and operating offshore hubs for renewable energy. As you will hopefully agree, we are by no means saying that it cannot be done. It can. But only if decisions are based on a solid foundation of knowledge.

The ability to evaluate pure compound properties of various molecular species is an important prerequisite for process simulation in general and in particular for computer-aided molecular design (CAMD). Current techniques rely on group-contribution methods, which suffer from many drawbacks mainly the absence of contributions for specific groups. To overcome this challenge, in this work, we extended the range of interpretable graph neural network models for describing a wide range of pure component properties. The new model library contains 30 different properties ranging from thermophysical, safety-related, and environmental properties. All of these have been modeled with a suitable level of accuracy for compound screening purposes compared to current group-contribution models used within CAMD applications. Moreover, the developed models have been subjected to a series of sanity checks using logical and thermodynamic constraints. Results show the importance of evaluating the model across a range of properties to establish their thermodynamic consistency.

Production planning is widely studied, and mathematical formulations have been proposed for various problems. The capacity level is usually fixed which requires a separate solution of the capacity planning problem. In this paper, a methodology is proposed for setting up and solving integrated capacity and production planning problems for a given manufacturing network. Mixed-integer linear programming problems are assembled from a constraint and objectives database based on a system characterization and their solution determines whether capacity levels should be changed. An indirect approach to solving the problem is suggested, in which the required capacities are determined with no knowledge of specific ways on how the capacity could be changed corresponding to an early stage of capacity planning. This contrasts with the traditional direct approach, in which a set of options are given and a subset of these chosen through optimization. The methodology is applied to three case studies from primary pharmaceutical manufacturing. A literature example is used to validate the method and two larger case studies show the method's ability to solve problems of industrial scale and relevance.

Industrial-sized fermenters differ from the laboratory environment in which bioprocess development initially took place. One of the issues that can lead to reduced productivity on a large scale or even early termination of the process is the presence of bioreactor heterogeneities. This work proposes and adopts a design–build–test–learn-type workflow that estimates the substrate, oxygen, and resulting growth heterogeneities through a compartmental modelling approach and maps Yarrowia lipolytica-specific behavior in this relevant range of conditions. The results indicate that at a growth rate of 0.1 h−1, the largest simulated volume (90 m3) reached partial oxygen limitation. Throughout the fed-batch, the cells experienced dissolved oxygen values from 0 to 75% and grew at rates of 0 to 0.2 h−1. These simulated large-scale conditions were tested in small-scale cultivations, which elucidated a transcriptome with a strong downregulation of various transporter and central carbon metabolism genes during oxygen limitation. The relation between oxygen availability and differential gene expression was dynamic and did not show a simple on–off behavior. This indicates that Y. lipolytica can differentiate between different available oxygen concentrations and adjust its transcription accordingly. The workflow presented can be used for Y. lipolytica-based strain engineering, thereby accelerating bioprocess development.