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The partial denitrification (PD) coupled with anaerobic ammonium oxidation (Anammox) (PD/A) process is a unique biological denitrification method for sewage that concurrently removes nitrate (NO 3 −-N) and ammonium (NH 4 +-N) in sewage. Comparing PD/A to conventional nitrification and denitrification technologies, noticeable improvements are shown in energy consumption, carbon source demand, sludge generation and emissions of greenhouse gasses. The PD is vital to obtaining nitrites (NO 2 −-N) in the Anammox process. This paper provided valuable insight by introduced the basic principles and characteristics of the process and then summarized the strengthening strategies. The functional microorganisms and microbial competition have been discussed in details, the S-dependent denitrification-anammox has been analyzed in this review paper. Important factors affecting the PD/A process were examined from different aspects, and finally, the paper pointed out the shortcomings of the coupling process in experimental research and engineering applications. Thus, this research provided insightful information for the PD/A process's optimization technique in later treating many types of real and nitrate-based wastewater. The review paper also provided the prospective economic and environmental position for the actual design implementation of the PD/A process in the years to come. [Display omitted] • PD/A was considered the most economically and environmentally beneficial new nitrogen removal process. • Principles and recent advances of the PD/A process are discussed. • The functional microorganisms and their competition in PD/A process are analyzed. • Influencing factors control and reinforcement strategies of PD/A was classified and conceptualized. • Knowledge gaps and prospects are presented to improve the future of PD/A process. [ABSTRACT FROM AUTHOR]

Minimising energy consumption in today's industrial sector is a crucial objective for achieving established climate goals. One effective strategy to enhance efficiency is optimising energy system operations within industries. The initial step in establishing operational optimisation involves developing a comprehensive model of the energy system. This model necessitates a specific structure to meet optimisation requirements. However, creating a model from scratch incurs substantial effort. While numerous companies possess energy models, they often lack the requisite structure for optimisation. Consequently, simplifying existing models can significantly reduce the effort needed to implement operational optimisation. This paper investigates the simplification of intricate industrial energy system models for optimisation purposes. The subsequent sections analyse two distinct approaches. One approach involves linearisation, while the other utilises neural networks. To facilitate a comparative analysis of these approaches, a reference model is developed. The assessment of these methodologies includes an investigation into optimisation robustness, computation time, accuracy concerning the reference model, and the effort required for developing and maintaining the simplified models. It proved that both approaches are suitable for operational optimisation. Linearisation exhibits superior computational efficiency compared to the neural network approach. The linearisation modelling approach together with the optimisation only required a few milliseconds for the calculation. The neural network approach needed 3 h for the calculation of the optimum with the genetic algorithm. The simulation of the neural network itself only required a few milliseconds. Hence, an improvement of the genetic algorithm is needed. However, the accuracy of linearisation falls short of that achieved by neural networks. The linearisation achieves a mean average percentage error from only 13%. In comparison the neural network's mean average error is 2.3%. Therefore, the linearisation must be improved. The impact using a piecewise linearisation on the results will be analysed in further research. [ABSTRACT FROM AUTHOR]

Due to the imperative nature of addressing climate change, the energy transition is currently underway, prompting the recognition of its urgency. Energy system optimisation models have emerged as crucial tools to assist policymakers in formulating laws and regulations that facilitate the transition towards carbon neutrality. While numerous models have been developed to explore various scenarios and define long-term objectives, only a few models focus on optimising the specific pathway to achieve these objectives. Many existing models lack the necessary time resolution to capture the integration of intermittent renewable energies; or are not open-source, creating a challenge in terms of transparency and reproducibility. This paper introduces EnergyScope Pathway, an open-source and documented model that addresses these limitations. It specifically optimises investment strategies for the whole-energy system over a 30-year period, or more, and optimising its hourly operation. This approach allows for a comprehensive evaluation of the effective integration of intermittent renewable energy sources. The model has a concise and efficient formulation, enabling its execution on personal laptops within approximately 15 min. By applying the model to the case study of Belgium, which presents challenges due to limited potential for renewable energy, we illustrate the importance of four pillars: energy efficiency, renewable energies, sector coupling, electrification and imports. The result pave the way to a new and incremental tool to support decision makers. In comparison to non open-source models, we verified the model's results with similar studies and found consistency in terms of technico-economic estimations. • Linear formulation of an optimisation model to represent a whole energy system transition pathway with an hourly resolution. • Short computational time, 15 min on a personal laptop • Quantify the pillars of the transition: efficiency, renewable energies and sector coupling. • Offers a new and incremental tool to support decision-makers in the energy transition. • Comparison of different approach to represent the transition. [ABSTRACT FROM AUTHOR]