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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]

• A user-friendly Abaqus/CAE plug-in tool is developed to rapidly estimate crashworthiness by inputting geometry parameters of these multi-cell tubes. • The offset and zoom can optimize the crashworthiness of the 0th order hybrid tubes. • The crashworthiness of first-order hybrid tubes can be enhanced by self-similar inspired double space-filling. • COPRAS and discrete optimization method are used for design and optimization. Self-similar inspired multi-cell tubes have been widely used in energy absorbing devices due to their flexible and feasible design strategy. In this paper, a novel multi-cell hexagonal tube is constructed by space-filling and cross-section topological design of self-similar inspired multi-cell tubes. A user-friendly plug-in tool is developed in Abaqus/CAE to rapidly estimate the crashworthiness of these multi-cell tubes by taking as input the geometry of these multi-cell tubes. The discrete robust optimization algorithm and multi-object decision-making method (COPRAS) are used to select the optimal cross-section topology of the self-similar inspired multi-cell hexagonal tubes. In addition, the effect of different space-filling strategies and the number and layout of ribs on the crashworthiness of these multi-cell tubes are studied. The results show that the crashworthiness of these multi-cell structures can be further improved by double space-filling. Hence, these design strategies and optimization methods proposed in this paper can be used in the topology optimization of the multi-cell tubes. [ABSTRACT FROM AUTHOR]

Due to its practicality, hybrid flowshop scheduling problem (HFSP) with productivity objective has been extensively explored. However, studies on HFSP considering green objective in distributed production environment are quite limited. Moreover, the current manufacturing mode is gradually evolving toward distributed co-production mode. Thus, this paper investigated a distributed hybrid flowshop scheduling problem (DHFSP) with objectives of minimization the makespan and total energy consumption (T E C). To address this problem, this paper designed a Pareto-based multi-objective hybrid iterated greedy algorithm (MOHIG) by integrating the merits of genetic operator and iterated greedy heuristic. In this MOHIG, firstly, one cooperative initialization strategy is proposed to boost initial solutions' quality based on the previous experience and rules. Secondly, one knowledge-based multi-objective local search method is invented to enhance the exploitation capability according to characteristics of problem. Thirdly, an energy-saving technique is developed to decrease the idle energy consumption of machine tools. Furthermore, the effectiveness of each improvement component of MOHIG is assessed by three common indicators. Finally, the proposed MOHIG algorithm is compared with other multi-objective optimization algorithms, including SPEA2, MOEA/D, and NSGAII. Experimental results indicate that the proposed MOHIG outperforms its compared algorithms in solving this problem. In addition, this research can better guide practical production in some certain environments. • Considering green scheduling in distributed hybrid flowshop environment. • Designing a new energy-saving strategy into this problem. • Proposing a Pareto-based multi-objective hybrid iterated greedy algorithm (MOHIG). • Evaluating performance of the proposed MOHIG by conducting comparison experiments. [ABSTRACT FROM AUTHOR]

Energy performance improvement in manufacturing is hindered by the lack of information on energy use in production and by the predominant economic vision in the assessment of energy interventions. This paper proposes a framework that integrates energy performance mapping with energy systems optimization. The framework introduces a new dimension to energy performance mapping by tracing energy use along processing and energy conversion steps and revisits multi-criteria assessments by assigning penalties to the energy performance gap with best practice. The suitability of food manufacturing for the application of the framework is exemplified from both a top-down and bottom-up perspective. More than 80% reductions in purchased energy carriers and non-renewable energy sources are achieved in a representative process through interventions coupling sector-specific manufacturing knowledge and energy expertise. With regard to one of the interventions, the introduction of the penalty is shown to shift the optimal solution, yielding more than 25% reductions in energy consumption compared to a purely economic assessment. The proposed framework can help industrial stakeholders identify improvement opportunities and develop more sustainable energy systems in manufacturing. Its widespread use can encourage poor performing companies to align with best practice and virtuous companies to continuous innovation to maintain their competitive advantage. [Display omitted] • A framework for energy performance improvement in manufacturing is proposed. • The framework introduces a new dimension to energy performance mapping. • The framework revisits multi-criteria assessments with an applicative orientation. • 80% reductions in purchased and non-renewable energy are achieved in a case study. • The introduction of the penalty yields 25% energy reduction in the optimal solution. [ABSTRACT FROM AUTHOR]

Multi-hop D2D communication has been proposed with the purpose of improving the coverage, quality of service (QoS), and flexibility and adaptability of single-hop D2D communication. However, multi-hop D2D communication often experiences obstacles caused by interference from the shared channel, which makes spectrum efficiency in multi-hop D2D networks an important issue to tackle. In this paper, we study the optimization of spectrum efficiency in multi-hop D2D communication underlaying cellular networks. First, we use iteration-based optimization techniques such as exhaustive search (ES) and gradient search (GS) with barrier function to find the global and local optimal solutions, respectively. More importantly, we propose two machine learning (ML) techniques, the unsupervised deep neural network (DNN) and deep Q-learning (DQL) algorithms and evaluate the performances of both algorithms compared to iteration-based optimization methods. The simulation results verify that both algorithms achieve near-global optimums compared to GS. Moreover, it is verified that the DQL outperforms the unsupervised DNN in terms of optimal spectrum efficiency, while the DQL algorithm has higher time complexity than the unsupervised DNN. • Evaluate energy efficiency of wireless 'multi-hop' D2D underlay cellular networks. • Build an optimization model for energy efficiency under given environment. • Design unsupervised DNN and deep Q-learning to solve the optimization model. • ML-based algorithms achieve near-global optimal solutions with lower time complexity. [ABSTRACT FROM AUTHOR]

This paper studies a novel guidance framework of the vehicle against a high-speed and maneuvering target based on deep reinforcement learning (DRL) considering the energy consumption, autopilot lag dynamics, and input saturation, which can effectively cope with the high flight-path angle error flight phase and various uncertainties. The guidance framework proposes an end-to-end mapping transformation between the guidance command and observation states consisting of line-of-sight (LOS) angle, relative distance, and their rate measured by the seeker. At the same time, the observability of the LOS angle and relative distance is included in constructing the reward function. Besides, the relative engagement kinematic model of the interceptor-target is established and combined with the PPO guidance algorithm, jointly described as a Markov decision process (MDP). Notably, the guidance framework is optimized using the improved proximal policy optimization (PPO) algorithm and demonstrated in a simulated terminal phase in the near-space. Specifically, the PPO guidance algorithm is structured by the policy (actor) neural network and the critic neural network, and both are standard fully-connected neural networks. Subsequently, observation states and rewards are fully collected and applied by introducing the experience replay method. Also, the exponential decay learning rate method, mini-batch stochastic gradient ascent (SGA) method, zero-score standardization, and Adam optimizer are proposed to train the reinforcement learning algorithm more efficiently. Moreover, the proposed guidance framework has an excellent generalization capability and guarantees the implementation of fixed and stochastic engagement scenarios, which means that the interceptor can realize the unlearned practical combat scenarios. The robust capacity is indicated and validated using Monte Carlo simulation under various uncertainties. Moreover, the DRL guidance framework can satisfy the onboard application requirement. [ABSTRACT FROM AUTHOR]

• Two seasonal-regulatable solar energy utilization systems have been proposed. • Comprehensive performance is greatly enhanced via multi-objective optimization. • Solar year-round effective utilization period can be enhanced by 2.63-fold. • The investment is dramatically reduced by over 70% under the same conditions. Space heating via fossil energy accounts for tremendous energy consumption and carbon emissions. Solar energy has enormous potential for building space heating in the heating season, but the more abundant solar resource is often in excess during the non-heating season, which leads to vast seasonal residual solar energy being wasted. In this paper, two solar seasonal-regulatable energy systems are proposed to solve this problem, i.e., System A: solar thermal and photovoltaic integration, and System B: solar thermal incorporating the organic Rankine cycle. Both systems are aimed at regulating the year-round solar energy for space heating in the heating season and making full use of solar seasonal residual energy for electricity generation in the non-heating season. The results show that the two systems can improve the effective solar utilization efficiency by 69.12% and 18.65%, respectively, and both can enhance the solar effective utilization period by 2.63-fold over the conventional solar thermal system. Besides, in comparison to the actual solar seasonal storage system monitoring data, both systems will dramatically decrease the investment cost by 72.69% and 72.22%, respectively. Overall, the superiority of solar seasonal-regulatable energy management systems in district-level applications is proven and it is instrumental in achieving the decarbonization goal. [ABSTRACT FROM AUTHOR]

• A novel invasive weed optimization algorithm based on levy flight (LFIWO) is proposed. • Nine different layout systems are proposed for optimizing wind turbine placement. • A new repair operator is developed in order to make the candidate solutions feasible. • Experimental studies are performed with ten binary variants for both of IWO and LFIWO. • LFIWO obtains the better results than those of the other algorithms in the literature. Wind energy has attracted great attention in recent years due to the increasing demand for alternative energy sources. Gathering the maximum amount of energy from wind energy is directly related to the layout of wind turbines in wind farm. This study focuses on grid-based wind turbine layout problem in an area of 2 km × 2 km. In order to solve this problem, 9 novel grids of 11 × 11, 12 × 12, ... and 19 × 19 are proposed in this paper in addition to 10 × 10 and 20 × 20 turbine grids which have already used in the literature. A new repair operator is recommended, taking into account the distance constraint between two adjacent cells in turbine layout problem, and thus the obtained solutions are made feasible. Furthermore, a levy flight-based IWO (LFIWO) algorithm is developed to optimize the layout of the turbines in wind farm. The basic IWO algorithm and the proposed LFIWO algorithm are compared on 11 different turbine layouts. Experimental studies are carried out for both algorithms under equal conditions with the help of 10 different binary versions. According to the comparisons performed using 11 different grids, LFIWO demonstrates a much superior success than the IWO algorithm. In addition, the proposed 19 × 19 grid reveals the best success among the other grids. When compared to the other studies in the literature, it is seen that LFIWO surpasses the other algorithms in terms of solution quality. As a result, it can be clearly stated that the proposed binary version of LFIWO is a competitive and effective binary algorithm. [ABSTRACT FROM AUTHOR]

• We present a novel way to consider efficiency in energy system optimization models. • Our approach can consider a range of behaviors consistent with microeconomic theory. • This new approach is applied to a simple test case and includes sensitivity analysis. • Maximum recovered welfare from efficiency crediting is <50% of the first-best option. Energy system optimization models (ESOMs) are designed to examine the potential effects of a proposed policy, but often represent energy-efficient technologies and policies in an overly simplified way. Most ESOMs include different end-use technologies with varying efficiencies and select technologies for deployment based solely on least-cost optimization, which drastically oversimplifies consumer decision-making. In this paper, we change the structure of an existing ESOM to model energy efficiency in way that is consistent with microeconomic theory. The resulting model considers the effectiveness of energy-efficient technologies in meeting energy service demands, and their potential to substitute electricity usage by conventional technologies. To test the revised model, we develop a simple hypothetical case and use it to analyze the welfare gain from an energy efficiency subsidy versus a carbon tax policy. In the simple test case, the maximum recovered welfare from an efficiency subsidy is less than 50% of the first-best carbon tax policy. [ABSTRACT FROM AUTHOR]