Energy system optimization model for tissue papermaking process

Methods for multi-objective investment and operating optimization of complex energy systems

In 1923, following years of opposition and debate, the City of San Francisco, Calif., completed the O'Shaughnessy Dam, which flooded Hetch Hetchy Valley in California's Yosemite National Park. Today the future of Hetch Hetchy Valley is still debated.Current discussion of restoring the valley has arisen within the state, and the idea of restoration has been largely supported by the use of system optimization and simulation modeling. Mathematical modeling provides thorough and systematic analysis of water supply systems and can suggest improvements to incorporate additional water uses or changing priorities. CALVIN (California Value Integrated Network),an economic‐engineering optimization model, was one of two models used to evaluate the water supply and hydro‐power implications of removing the O'Shaughnessy Dam [Null, 2003; Null and Lund, 2006].

We present a framework for capacity allocation decisions for assembly-test (A-T) facilities that is comprised of an optimization model and a simulation model. The optimization and simulation models are used iteratively until a feasible and profitable capacity plan is generated. The models communicate using an automated feedback loop and at each iteration the model parameters are adjusted. We describe the role of the optimization model, the simulation model and the feedback loop. Once the capacity plan is generated, it is passed down to the shop-floor for implementation. Hence, decision makers can develop accurate and more profitable execution level capacity plans using the integrated model which utilizes both optimization and simulation models. In this paper, we focus on the optimization model for capacity planning for the entire A-T facility at the individual equipment (resource) level for a two-week planning period and briefly discuss the simulation and the adjustment model.

Asymmetric Nash bargaining model for operation optimization of multi-integrated energy systems considering peer-to-peer energy trading

Due to the high energy consumption of buildings, there is a demand for both economically and environmentally effective designs for building energy system retrofits. While multi-objective optimization can be used to solve complicated problems, its use is not yet widespread in the industry. This study first aims to develop an efficient and applicable multi-objective building energy system optimization method, used to dimension energy production and storage retrofit components in a case campus building in Lahti, Finland. Energy consumption data of the building are obtained with a dynamic energy model. The optimization model includes economic and environmental objectives, and the approach is found to function satisfactorily. Second, this study aims to assess the feasibility and issues of multi-objective single-building energy system optimization via the analysis of the case optimization results. The results suggest that economically beneficial local energy production and storage retrofits could not always lead to life cycle CO2-eq emission reductions. The recognized causes are high life cycle emissions from the retrofit components and low Nordic grid energy emissions. The performed sensitivity and feasibility analyses show that correctness and methodological comparability of the used emission factors and future assumptions are crucial for reliable optimization results.

• A new control logic for the SCMSHPD system under low meteorological conditions was proposed. • Performance of the SCMSHPD system operating under the new control logic were analyzed. • The effects of the waste heat recovery on the combined drying system under different meteorological parameters were obtained. • Drying process of the combined drying system was optimized. To solve the problem that the high drying temperature demand could not be met when combined drying system operated under low ambient temperature and weak solar irradiance, a new control logic of the solar combined multi-heat source heat pump drying (SCMSHPD) system was proposed and its simulation model was established. By modifying its operating strategy, the SCMSHPD system achieved a higher drying temperature under low environment temperature and weak irradiance, which ensured the implementation of the drying process. The simulation results was verified through experimental study. Additionally, to address the issue of significant heat loss of the exhaust air, waste heat recovery was implemented and its impact on the system’s operating characteristics was analyzed. At last, the drying process was optimized due to the significant improvement of the specific moisture extraction rate ( SMER ) and the system performance was further improved. The results showed that the errors in coefficient of performance ( COP ), energy consumption, and SMER between the experimental and simulation results were 4.04%, 1.84% and 1.53% respectively, which verified the reliability of the simulation model. In October, the COP of the SCMSHPD system without waste heat recovery was 3.66% higher than that of the ASHPD system, but with the decrease of ambient temperature and solar irradiance, the COP of the combined drying system increased by 5.86% in December, demonstrating the advantage of the SCMSHPD system with the new control logic under low meteorological parameters. The waste heat recovery could not improve the system performance of the SCMSHPD system operating with the new control logic proposed, which caused a decrease of 1.06% and 1.19% in October and December respectively. However, the SMER was significantly increased by 70.67% and 12.78% respectively, and the energy consumption was significantly decreased by 41.33% and 10.94%. The results indicated that although waste heat recovery resulted in a slight decrease in system COP , the significant reduction in energy consumption and notable increase in SMER demonstrated a marked improvement in the overall system performance. Due to the significant increase of the SMER , the drying time was shortened, which caused an increase of 1.77% in COP in October. With meteorological parameters became lower, the COP get a bigger increase of 3.34%. Therefore, it could be concluded that the combination of waste heat recovery and shortened drying time could effectively improve the system performance, and the lower the meteorological parameters, the greater the improvement in the system performance. This study can provide important guidance for the selection of control logic and the optimization of the drying process for the SCMSHPD system under low meteorological parameters.

Carbon footprint in building distributed energy system: An optimization-based feasibility analysis for potential emission reduction

We review the state of the art of process modeling for discrete event simulation, make a number of observations and identify a number of issues that have to be tackled for promoting the use of process modeling in simulation. Process models are of particular interest in model-based simulation engineering approaches where the executable simulation model (code) is obtained with the help of textual or visual models. We present an illustrative example of model-based simulation development.

The characteristics of groundwater systems are highly complex. It will take substantial computational resources and running time to optimize a groundwater numerical simulation model. In this study, in order to realize the coupling of simulation and optimization models, the improved backpropagation (BP) neural network was used as a surrogate model of a groundwater numerical simulation; the improved BP neural network was trained with the groundwater level drawdown–pumping volume data output of the simulation model. The method was applied to the water resource optimal allocation in the near future of Wenshang County, Shandong Provence of China. The results show that the water level drawdown output of the improved BP neural network model fits the results of the simulation model well, showing that the improved BP neural network can effectively be the surrogate of a groundwater numerical simulation to be embedded in an optimization model. The improved simulation and optimization technique can make full use of water resources in the whole area. Under an assurance rate of 50%, both water shortage and water shortage rate reduced to zero in the whole area. Under an assurance rate of 75%, water shortage and water shortage rate reduced to about 10% of the conventional scheme, which dramatically improves the comprehensive benefit of the whole area.

The aim of the study was to estimate the optimal parameters of apple drying and the rehydration temperature of the obtained dried apple. Conducting both processes under such conditions is aimed at restoring the rehydrated apple to the raw material properties. The obtained drying parameters allow the drying process to be carried out in a short drying time (DT) and at low energy consumption (EC). The effect of air velocity (vd), drying temperature (Td), characteristic dimension (CD), and rehydration temperature (Tr) on rehydrated apple quality was studied. Quality parameters of the rehydrated apple as: color change (CC), mass gain ratio (MG), solid loss ratio (SL), volume gain ratio (VG) together with DT and EC were taken into consideration. The artificial neural network was used for modeling of rehydrated apple quality parameters, DT, and EC. A multi-objective genetic algorithm was developed in order to optimize parameters of the drying and rehydration processes. The simultaneous minimization of CC, SL, DT, EC, and the maximization of MG and VG were considered with the following drying and rehydration processes parameters: Td: 50–70 °C, vd: 0.01–2 m/s, Tr: 20–70 °C. The best solution has been found at drying temperature 56.1 °C, air velocity 1.3 m/s, characteristic dimension 2.0 mm, and rehydration temperature 59.2 °C. This apple drying and rehydration resulted in MG = 3.51, SL = 0.57, VG = 4.77, CC = 11.2, DT = 5.4 h, EC = 159.8 GJ/kg. The parameters of apple drying and rehydration processes can be recommended for the industry application.

Performance analysis of rotatable energy system of high-altitude airships in real wind field

Computational effort and convergence problems can pose serious challenges when employing advanced thermodynamic models in process simulation and optimization. Data-based surrogate modeling helps to overcome these problems at the cost of additional modeling effort. The present work extends the range of methods for efficient data-based surrogate modeling of liquid–liquid equilibria. A new model formulation is presented that enables smaller surrogates with box-constrained input domains and reduced input dimensions. Sample data are generated efficiently by using numerical continuation. The new methods are demonstrated for the surrogate modeling and optimization of a process for the hydroformylation of 1-decene in a thermomorphic multiphase system.

Comparison of the performance of a modified temperature-controlled microwave dryer to improve heat-mass transfer, increase energy efficiency and preserve quality characteristics of shad (Alosa fallax nilotica)

In this paper, aiming at the complex thermophysical interaction in the tobacco drying process, the thermo-physical coupling modeling and intelligent control algorithm are studied. Firstly, a three-field coupling model including heat conduction, moisture diffusion and tobacco deformation was constructed. Unsteady heat transfer equation, diffusion model modified by deformation tensor and generalized Maxwell model were used to characterize the internal temperature field, moisture diffusion and tobacco deformation characteristics respectively, and the parameters were identified and optimized by multi-scale model fusion and improved NSGA-II algorithm. Secondly, a dual-channel intelligent control architecture combining physical model apriori and deep learning correction is designed, and a deep reinforcement learning control strategy based on SAC (Soft Actor-Critical) algorithm and an online adaptive mechanism are introduced to realize precise control of the silk drying process. The experimental results show that, compared with traditional PID control, pure physical model control and pure LSTM control, the hybrid control method proposed in this paper has obvious advantages in water content control accuracy, energy consumption reduction and system robustness. The standard deviation of water content decreased below 0.055% and energy consumption is reduced by 21%, which provides effective technical support for the intelligent upgrade of cigarette shredding and drying process and has important engineering application value.

In the field of energy system modelling, increasing complexity and optimization analysis are essential for understanding the most effective decarbonization options. However, the growing need for intricate models leads to increased computational time, which can hinder progress in research and policy-making. This study aims to address this issue by integrating machine learning algorithms with EnergyPLAN and EPLANopt, a coupling of EnergyPLAN software and a multi-objective evolutionary algorithm, to expedite the optimization process while maintaining accuracy. By saving computational time, we can increase the number of evaluations, thereby enabling deeper exploration of uncertainty in energy system modelling. Although machine learning models have been widely employed as surrogate models to accelerate optimization problems, their application in energy system modeling at the national scale, while preserving high temporal resolution and extensive sector-coupling, remains scarce. Several machine learning models were evaluated, and an artificial neural network was selected as the most effective surrogate model. The findings demonstrate that incorporating this surrogate model within the optimization process reduces computational time by 64 % compared to the conventional EPLANopt approach, while maintaining an accuracy level close to that obtained by running EPLANopt without the surrogate model.