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Supercritical multicomponent thermal fluid injection is a new technology with great potential for offshore heavy oil thermal recovery. In the process of thermal fluid generation, the reaction conditions including temperature, pressure, and the organic mass concentration in the reaction material will significantly affect its composition and injection rate and will further affect the thermal recovery and development quality of heavy oil. However, there is a lack of relevant research on the variation rules and control methods of the composition and injection rate of supercritical multicomponent thermal fluids, resulting in a lack of technical mechanisms for effective optimization. To fill this gap, a reaction molecular dynamics simulation method was used to simulate thermal fluid generation under different temperatures, pressures, and organic mass concentrations. The changes in thermal fluid composition and yield with reaction conditions were studied, and a control model of thermal fluid composition and yield was established. The proportional relationship between the thermal fluid generation scale of an offshore heavy oil platform and the simulated thermal fluid generation scale is analyzed, and a collaborative optimization method of thermal fluid composition and injection rate in field applications is proposed. The results show the following: (1) The higher the mass concentration of organic matter, the higher the content of supercritical carbon dioxide and supercritical nitrogen in thermal fluids, and the lower the content of supercritical water. (2) The higher the temperature and pressure, the higher the thermal fluid yield, and the higher the organic mass concentration, the lower the thermal fluid yield. (3) The component fitting model conforms to the power function relationship, and the coefficient of determination R2 is greater than 0.9; the yield fitting model conforms to the modified inverse linear logarithmic function relationship, the determination coefficient R2 is greater than 0.8, and the fitting degree is high. (4) The ratio between the actual injection rate of thermal fluids in the mine field and the molecular simulated thermal fluid yield is the ratio of organic matter mass in the platform thermal fluid generator and organic matter mass in the simulated box. (5) Based on the composition and yield control model, combined with the simulation of the ratio relationship between yield and injection rate in the field, a collaborative optimization method of thermal fluid composition and injection rate was established. The research results can provide an effective technical method for predicting, controlling, and optimizing the composition and injection rate of supercritical multicomponent thermal fluids. [ABSTRACT FROM AUTHOR]

This study aims to investigate the potential and advantages of multi-agent reinforcement learning (MARL) in forest management, offering innovative insights and methodologies for achieving sustainable management of forest ecosystems. Focusing on the Pinus yunnanensis secondary forests in Southwest China, we formulated the objective function and constraints based on both spatial and non-spatial structural indices of the forest stand structure (FSS). The value of the objective function (VOF) served as an indicator for assessing FSS. Leveraging the random selection method (RSM) to select harvested trees, we propose the replanting foreground index (RFI) to enhance replanting optimization. The decision-making processes involved in selection harvest optimization and replanting were modeled as actions within MARL. Through iterative trial-and-error and collaborative strategies, MARL optimized agent actions and collaboration to address the collaborative optimization problem of FSS. We conducted optimization experiments for selection felling and replanting across four circular sample plots, comparing MARL with traditional combinatorial optimization (TCO) and single-agent reinforcement learning (SARL). The findings illustrate the superior practical efficacy of MARL in collaborative optimization of FSS. Specifically, replanting optimization based on RFI outperformed the classical maximum Delaunay generator area method (MDGAM). Across different plots (P1, P2, P3, and P4), MARL consistently improved the maximum VOFs by 54.87%, 88.86%, 41.34%, and 22.55%, respectively, surpassing those of the TCO (38.81%, 70.04%, 41.23%, and 18.73%) and SARL (54.38%, 70.04%, 41.23%, and 18.73%) schemes. The RFI demonstrated superior performance in replanting optimization experiments, emphasizing the importance of considering neighboring trees' influence on growth space and replanting potential. Following selective logging and replanting adjustments, the FSS of each sample site exhibited varying degrees of improvement. MARL consistently achieved maximum VOFs across different sites, underscoring its superior performance in collaborative optimization of logging and replanting within FSS. This study presents a novel approach to optimizing FSS, contributing to the sustainable management of Pinus yunnanensis secondary forests in southwestern China. [ABSTRACT FROM AUTHOR]

The robot task sequencing problem and trajectory planning problem are two important issues in the robotic optimization domain and are solved sequentially in two separate levels in traditional studies. This paradigm disregards the potential synergistic impact between the two problems, resulting in a local optimum solution. To address this problem, this paper formulates a co-optimization model that integrates the task sequencing problem and trajectory planning problem into a holistic problem, abbreviated as the robot TSTP problem. To solve the TSTP problem, we model the optimization process as a Markov decision process and propose a deep reinforcement learning (DRL)-based method to facilitate problem solving. To validate the proposed approach, multiple test cases are used to verify the feasibility of the TSTP model and the solving capability of the DRL method. The real-world experimental results demonstrate that the DRL method can achieve a 30.54% energy savings compared to the traditional evolution algorithm, and the computational time required by the proposed DRL method is much shorter than those of the evolutionary algorithms. In addition, when adopting the TSTP model, a 18.22% energy reduction can be achieved compared to using the sequential optimization model. [ABSTRACT FROM AUTHOR]

To address the shortcomings of the sine cosine algorithm such as the low search accuracy, slow convergence speed, and easily falling into local optimality, a sine cosine algorithm for elite individual collaborative search was proposed. Firstly, tent chaotic mapping was used to initialize the population and the hyperbolic tangent function was applied non-linearly to adjust the parameters of the sine cosine algorithm, which enhanced the uniformity of population distribution and balanced the global exploration and local exploitation ability. Secondly, the search method of the sine cosine algorithm was improved by combining the search strategy of the sine cosine algorithm, the m-neighborhood locally optimal individual-guided search strategy, and the global optimal individual-guided search strategy, and, then, the three search strategies were executed alternately, which achieved collaboration, improved the convergence accuracy, and prevented the algorithm from falling into local optima. Finally, a greedy selection strategy was employed to select the best individuals for the population, which accelerated the convergence speed of the sine cosine algorithm. The simulation results illustrated that the sine cosine algorithm for elite individual collaborative search demonstrated a better optimization performance than the sine cosine algorithm, the other improved sine cosine algorithms, the other chaos-based algorithms, and other intelligent optimization algorithms. In addition, the feasibility and applicability of the sine cosine algorithm for elite individual collaborative search were further demonstrated by two mechanical optimization design experiments. [ABSTRACT FROM AUTHOR]

The requirement for the integration of power plants due to the cyclical rise in electrical energy consumption is due to the fluctuating load demand experienced with the current grid systems. This integration necessitates effectively allocating loads to the power plants for a minimum grid-tied transmission line cost, while meeting the network constraints. In this paper, we formulate an optimization problem of minimizing the total operational cost of all committed plants transmitted to the grid, while also meeting the network constraints and ensuring economic power dispatch (EPD) and energy management system co-optimization. The developed particle swarm optimization (PSO) method resolves the optimization problem using a piecewise quadratic function to describe the operational cost of the generation units, and the B coefficient approach is employed to estimate the transmission losses. Intelligent adjustments are made to the acceleration coefficients, and a brand-new algorithm is suggested for distributing the initial power values to the generation units. The developed economic power dispatch strategy successfully demonstrated an imperative cost reduction, with a connected load of 850 MW, 1263 MW, and 2630 MW of power demand, contrasted with previous PSO application cost values percentage, maximum yearly cost savings of (0.55%, 91.87), (46.55%, 3.78), and (73.86%, 89.10), respectively, and significant environmental benefits. The proposed co-optimization approach can significantly enhance the self-consumption ratio compared to the baseline method. [ABSTRACT FROM AUTHOR]

Co-optimization for memory bank compilation and placement was suggested as a way to improve performance and power and reduce the size of a memory subsystem. First, a multi-configuration SRAM compiler was realized that could generate memory banks with different PPA by splitting or merging, upsizing or downsizing, threshold swapping, and aspect ratio deformation. Then, a timing margin estimation method was proposed for the memory bank based on placed positions. Through an exhaustive enumeration of various configuration parameters under the constraint of timing margins, the best SRAM memory compilation configuration was found. This method could be integrated into the existing physical design flow. The experimental results showed that this method achieved up to an 11.1% power reduction and a 7.6% critical path delay reduction compared with the traditional design method. [ABSTRACT FROM AUTHOR]

Carbon fiber is becoming a key material for engineering applications due to its excellent comprehensive properties. The process parameter optimization is an important step in the polymerization process of carbon fiber production. At present, most of the research on process parameter optimization is usually carried out on a single production line, without considering the correlation between optimization problems. In this paper, a multiobjective mechanism model for the co-optimization of the polymerization process of carbon fiber production is established. Each of these submodels is a multiobjective process parameter optimization task, corresponding to the polymerization process of a production line. In order to solve the model effectively, we also designed an evolutionary multitasking algorithm based on transfer learning, which reuses the past experiences of one task to generate a population pool for the next iteration of another task, enabling explicit genetic transfer between different tasks and accelerating the population convergence speed. The proposed multitasking framework for operation optimization has been conducted on 10 different production conditions of the polymerization process. Experimental results show that compared with other implicit and explicit genetic algorithms, this algorithm is very competitive in generating effective solutions. This research provides important support for process parameter optimization and manufacturing of carbon fiber production, which will help engineers and technicians to make informed decisions. [ABSTRACT FROM AUTHOR]

With increasing concerns on transportation decarbonization, fuel cell hybrid trains (FCHTs) attract many attentions due to their zero carbon emissions during operation. Since fuel cells alone cannot recover the regenerative braking energy (RBE), energy storage devices (ESDs) are commonly deployed for the recovery of RBE and provide extra traction power to improve the energy efficiency. This paper aims to minimize the net hydrogen consumption (NHC) by co-optimizing both train speed trajectory and onboard energy management using a time-based mixed integer linear programming (MILP) model. In the case with the constraints of speed limits and gradients, the NHC of co-optimization reduces by 6.4% compared to the result obtained by the sequential optimization, which optimizes train control strategies first and then the energy management. Additionally, the relationship between NHC and employed ESD capacity is studied and it is found that with the increase of ESD capacity, the NHC can be reduced by up to 30% in a typical route in urban railway transit. The study shows that ESDs play an important role for FCHTs in reducing NHC, and the proposed time-based co-optimization model can maximize the energy-saving benefits for such emerging traction systems with hybrid energy sources, including both fuel cells and ESD. [ABSTRACT FROM AUTHOR]

The non-ideal mask absorber can cause an increase in critical dimension error (CDE) and decrease in process window (PW). However, the random mask absorber errors induced during mask fabricating and measuring are not considered in computational lithography. The problem cannot be neglected as the continuous scaling of lithography technology node. In this work, for the first time to our knowledge, a source, numerical aperture (NA), and process parameters co-optimization (SNPCO) method is developed to reduce the CDE induced by absorber errors and improve the PW. First, the source is represented by Zernike polynomials to balance computational burden and flexibility of source. Then a weighted cost function containing CDE and PW that incorporates the influences of absorber errors is created. Finally, a statistical optimization method is used to optimize the lithographic system parameters. Simulations of 1D mask pattern show that for the system with extreme absorber errors, the pattern errors of the proposed method are reduced by 62.1% and 58.9%, and the PWs are increased by 40.3% and 36.4%, respectively. The results illustrate that this method is effective in mitigating the CDE caused absorber errors and improving process robustness. [ABSTRACT FROM AUTHOR]

Due to the variable nature of wind resources, the increasing penetration level of wind power will have a significant impact on the operation and planning of the electric power system. Energy storage systems are considered an effective way to compensate for the variability of wind generation. This paper presents a detailed production cost simulation model to evaluate the economic value of compressed air energy storage (CAES) in systems with large-scale wind power generation. The co-optimization of energy and ancillary services markets is implemented in order to analyze the impacts of CAES, not only on energy supply, but also on system operating reserves. Both hourly and 5-minute simulations are considered to capture the economic performance of CAES in the day-ahead (DA) and real-time (RT) markets. The generalized network flow formulation is used to model the characteristics of CAES in detail. The proposed model is applied on a modified IEEE 24-bus reliability test system. The numerical example shows that besides the economic benefits gained through energy arbitrage in the DA market, CAES can also generate significant profits by providing reserves, compensating for wind forecast errors and intra-hour fluctuation, and participating in the RT market [ABSTRACT FROM AUTHOR]

This article explores the complex relationship between environmental regulation, innovation, and sustainable development within the context of an increasingly globalizing economy. The economic development, environment, and employment aspects of sustainable development are emphasized. We contend that the most crucial problem in achieving sustainability is lock-in or path dependency due to (1) the failure to envision, design, and implement policies that achieve co-optimization, or the mutually reinforcing, of social goals, and (2) entrenched economic and political interests that gain from the present system and advancement of its current trends. The article argues that industrial policy, environmental law and policy, and trade initiatives must be 'opened up' by expanding the practice of multi-purpose policy design, and that these policies must be integrated as well. Sustainable development requires stimulating revolutionary technological innovation through environmental, health, safety, economic, and labor market regulation. Greater support for these changes must also be reinforced by 'opening up the participatory and political space' to enable new voices to contribute to integrated thinking and solutions. [ABSTRACT FROM AUTHOR]

For energy sustainability, the integrated energy distribution system (IEDS) is an efficient and clean energy system, which is based on the coordinated operation of a power distribution network, a gas distribution network and a district heating system. In this paper, considering the damage of natural disasters to IEDS, a demand management strategy is proposed to improve resilience of IEDS and ensure stable operation, which is divided into three stages. In the first stage, the electricity, natural gas and thermal energy are co-optimized in the simulating fault state to develop the importance ranking of transmission lines and gas pipelines. In the second stage, the natural disasters are classified as surface natural disasters and geological natural disasters. According to the types of natural disasters, the demand management strategy includes semi-emergency demand management scheme and full-emergency demand management scheme in the electrical resilience mode and the integrated resilience mode, respectively. In the third stage, the non-sequential Monte-Carlo simulation and scenario reduction algorithm are applied to describe potential natural disaster scenarios. According to the importance ranking of transmission lines and gas pipelines, a demand management strategy is formulated. Finally, the proposed strategy is applied on an IEEE 33-bus power system and a 19-node natural gas system. Its effectiveness is verified by numerical case studies. [ABSTRACT FROM AUTHOR]

One reason for the allocation of reserves in electricity markets is the uncertainty of demand and supply. If the bias of the generation portfolio shifts from controllable generators to renewable sources with significantly higher uncertainty, it is natural to assume that more reserve has to be allocated. The price of reserve allocation in European models is dominantly paid by the independent system operator in the form of long-term paid reserve capacities and reserve demand bids submitted to various reserve markets. However, if we consider a scenario where the significant part of generation is allocated in day-ahead auctions, the power mix is not known in advance, so the required reserves can not be efficiently curtailed for the ratio of renewables. In the current paper we analyze an integrated European-type, portfolio-bidding energy-reserve market model, which aims to (at least partially) put the burden of reserve allocation costs to the uncertain energy bidders who are partially responsible for the amount of reserves needed. The proposed method in addition proposes a more dynamic and adaptive reserve curtailment method compared to the current practice, while it is formulated in a computationally efficient way. [ABSTRACT FROM AUTHOR]

This study evaluates the potential of carbon dioxide-enhanced oil recovery (CO2-EOR) to reduce greenhouse gas emissions without compromising oil production goals. A novel, dynamic carbon lifecycle analysis (d-LCA) was developed and used to understand the evolution of the environmental impact (CO2 emissions) and mitigation (geologic CO2 storage) associated with an expanded carbon capture, utilization and storage (CCUS) system, from start to closure of operations. EOR operational performance was assessed through CO2 utilization rates, which relate usage of CO2 to oil production. Because field operational strategies have a significant impact on reservoir engineering parameters that affect both CO2 storage and oil production (e.g., sweep efficiency, flood conformance, fluid saturation distribution), we conducted a scenario analysis that assessed the operational and environmental performance of four common and novel CO2-EOR field development strategies. Each scenario was evaluated with and without stacked saline carbon storage, an EOR/storage combination strategy where excess CO2 from the recycling facility is injected into an underlying saline aquifer for long-term carbon storage. The dynamic interplay between operational and environmental performance formed the basis of our CCUS technology analysis. The results showed that all CO2-EOR evaluated scenarios start operating with a negative carbon footprint and, years into the project, transitioned into operating with a positive carbon footprint. The transition points were significantly different in each scenario. Water-alternating-gas (WAG) was identified as the CO2 injection strategy with the highest potential to co-optimize EOR and carbon storage goals. The results provide an understanding of the evolution of the system's net carbon balance in all four field development strategies studied. The environmental performance can be significantly improved with stacked storage, where a negative carbon footprint can be maintained throughout the life of the operation in most of the injection scenarios modelled. This information will be useful to CO2-EOR operators seeking value in storing more CO2 through a carbon credit program (e.g., the 45Q carbon credit program in the USA). Most importantly, this study serves as confirmation that CO2-EOR can be operationally designed to both enhance oil production and reduce greenhouse gas emissions into the atmosphere. [ABSTRACT FROM AUTHOR]

This paper proposes a system model for optimal dispatch of the energy and reserve capacity considering uncertain load demand and unsteady power generation. This implicates uncertainty in managing the power demand along with the consideration of utility, user and environmental objectives. The model takes into consideration a day-ahead electricity market that involves the varying power demand bids and generates a required amount of energy in addition with reserve capacity. The lost opportunity cost is also considered and incorporated within the context of expected load not served. Then, the effects of combined and separate dispatching the energy and reserve are investigated. The nonlinear cost curves have been addressed by optimizing the objective function using robust optimization technique. Finally, various cases in accordance with underlying parameters have been considered in order to conduct and evaluate numerical results. Simulation results show the effectiveness of proposed scheduling model in terms of reduced cost and system stability. [ABSTRACT FROM AUTHOR]

After disasters, enhancing the resilience of power systems and restoring power systems rapidly can effectively reduce the economy damage and bad social impacts. Reasonable post-disaster restoration strategies are the most critical part of power system restoration work. This paper co-optimizes post-disaster damage repair and power system operation together to formulate the optimal repair route, the unit output and transmission switching plan. The power outage loss will be minimized, with possible small expense of damage repair and power system operation cost. The co-optimization model is formulated as a mixed integer second order cone program (MISOCP), while the AC-power-flow model, the complex power system restoration constraints and the changing processes of component available states are synthetically considered to make the model more realistic. Lagrange relaxation (LR) decomposes the model into the damage repair routing sub problem and the power system operation sub problem, which can be solved iteratively. An acceleration strategy is used to improve the solving efficiency. The proposed model and algorithm are validated by the IEEE 57-bus test system and the results indicate that the proposed model can realize the enhancement of resilience and the economic restoration of post-disaster power systems. [ABSTRACT FROM AUTHOR]