Cost Rate Research Articles

A novel TD3 for solving multi-level imperfect maintenance optimization problem

Abstract As we all know, multi-level imperfect maintenance strategy is usually more effective than single-level maintenance strategy for the actual production machines. At the same time, in the previous multi-level maintenance strategies, the majority of maintenance models only consider the constant maintenance thresholds, while variable maintenance thresholds are usually ignored. Under these contexts, a novel multi-level imperfect maintenance model with variable preventive maintenance (PM) thresholds, variable overhaul maintenance (OM) thresholds and variable number of PMs in each OM cycle is established. In order to deal with the concerned problem, a novel twin delayed deep deterministic policy gradient (TD3) algorithm that is a kind of reinforcement learning is designed, renamed as NTD3. Finally, through numerical simulation, we can find that (1) the average improvements between the proposed maintenance strategy and other three traditional strategies in the average cost rate (ACR) are 11.50%, 595.91% and 5.16%, respectively; and (2) the average improvement between the proposed NTD3 and other random search method is 5.53%. Thus, the effectiveness of the proposed maintenance strategy and the superiority of proposed NTD3 are all demonstrated.

Multi-criteria optimization next to a comparative analysis of a polygeneration layout based on a double-stage gas turbine cycle fueled by biomass and hydrogen

The significance and advantages of utilizing renewable energy sources are widely recognized. Upon examining past studies, it was revealed that there has been a lack of research on harnessing the waste energy from double-stage gas turbines powered by biomass-hydrogen hybrid fuel for polygeneration applications. Thus, a new configuration is proposed in the study for the multi-production of electricity, cooling, heating, and potable water. In the current research, a high-pressure turbine is driven by a combustion chamber fed by a biomass gasifier. Meanwhile, a low-pressure turbine is driven by a post-combustion chamber fed by hydrogen as fuel. The heat loss of the topping gas turbine cycle is recuperated by a supercritical Brayton cycle (SBC), a steam Rankine cycle (SRC), and a water heater. The waste heat of the supercritical Brayton cycle and steam Rankine cycle is recovered through an absorption refrigeration cycle and a thermoelectric generator, respectively. Thus, the heat loss of the topping system is completely recovered. The net power output of the gas turbine cycle is considered the electricity production of the system while the output power of the SBC and SRC is utilized in a reverse osmosis desalination system to produce potable water. The investigations conducted on the system are thermodynamic and exergy-economic assessments and three-objective optimization utilizing 5 gases (i.e., CO2, CO, Nitrogen, Air, and Helium) in the SBC. The final results reveal the superiority of Helium as the supercritical gas, bringing about an exergy efficiency () of 32.11%, a total cost rate () of 103 , a payback period (PP) of 0.159 years, and a specific cost of polygeneration () of 22.03 for the system. Although the levelized cost of products of the configuration is high due to the freshwater production, of the proposed system is higher than the previous systems based on a double-stage gas turbine cycle driven by biomass and hydrogen.

4E analysis of novel waste heat-driven combined cooling and power (CCP) systems based on modified Kalina-ejector refrigeration cycles

Global warming and environmental pollution are becoming increasingly severe problems due to the excessive use of fossil fuels. Recovering and utilizing waste heat from different energy systems can considered a sustainable solution to this destructive trend. This study proposes two modified Kalina cycle structures that use ejector refrigeration for combined cooling and power (CCP) generation in low-temperature waste heat. While to recover energy from a lean ammonia solution uses a two-phase expander. Thermodynamic analysis shows that the modified Kalina cycles E and F operate with a thermal efficiency of 0.3066 and 0.2557, respectively, representing a significant improvement of more than 123% and 86%, respectively, compared to the base cycle. Also, the exergy efficiency of the improved configurations E and F has been calculated as 0.083 and 0.089, respectively, showing an improvement of 53.8% and 64.8%, respectively, compared to the base cycle. The exergoeconomic analysis also revealed that the total capital cost rate for improved cycles E and F is estimated at 2.62 and 2.76 $/h, respectively. In addition, from an environmental perspective, the improved Kalina cycle F has proven to have a more eco-friendly performance with a pollution index of 0.2281 mPts/s. Moreover, a parametric study is conducted to investigate and analyze the effect of changes in significant performance parameters such as ammonia concentration, evaporator temperature, turbine inlet temperature and pressure, and turbine back-pressure for both presented modified cycles.

Thermoeconomic modeling of new energy system based on biogas upgrading to multigenerational purpose

The following article outlines an integrated approach toward the valorization of biogas for energy, chilled water, hot water, freshwater, and liquid carbon dioxide production. The suggested system includes a biogas upgrading unit with a biomethane-fueled burner, a Kalian cycle, an LNG regasification process, and a multi-effect desalination unit that can provide CO2, electricity, cooling, and heating. The new ideas in the proposed system include a cryogenic biogas upgrading process that can be used in many industrial settings, the production of liquid carbon dioxide through biogas upgrading, and the use of biomethane in the integrated combustion process. Furthermore, the grey wolf optimization technique is applied in various multi-objective optimization contexts to achieve optimal operating conditions and preferred performance outcomes. In this simulation, the generation of electricity reaches a value of 755 kW. Attached to it is a cooling load of about 5102 kW, with a heating load of 4725 kW, freshwater capacity about 1.33 kg/s, production rate of liquid CO2 at 0.58 kg/s, and natural gas at 3.1 kg/s. Thermodynamic results reveal that the proposed multigeneration performance scheme providently enhances energy and exergy efficiencies up to 59.82 % and 5.74 %, respectively, compared to the condition of a single product. The performed exergy analysis shows that the overall irreversibility of the suggested configuration is equal to 18,047 kW while about 42.6 % of this value is contributed from the heat exchanger E-104. Moreover, the CO2 emission intensity of the process can be kept as 0.35 kg/kWh. Cryogenic separation can obtain about 88 % of CO2 in biogas and turn it into its liquid phase. The performed economic assessment has resulted in the following total cost rate as well as cost per unit exergy: 321.38 $/h and 83.06 $/GJ, respectively. Under optimal conditions, an exergy efficiency of 13.40 % and a net power output of 2034.39 kW are attained.

Techno-economic analysis and optimization of a hybrid solar-wind-biomass-battery framework for the electrification of a remote area: A case study

An off-grid hybrid energy framework on the basis of wind turbines and photovoltaic panels as the primary source of energy and a biogas generator and energy storage unit as a back-up system, was considered for clean energy generation in a rural area in Semnan, Iran. This study was aimed at designing an optimized hybrid renewable framework with minimal costs and CO2 emissions and highest reliability, utilizing comprehensive modeling based on two energy management strategies. The decision-making variables in this work were the type and number of wind turbines, number of storage units, capacity of solar panels, and the capacity of biogas generator. Then variations in turbine model, inflation rate and biofuel prices and their effect on the optimal design of the hybrid framework was investigated in a range of uncertainties (0, 1, and 2.5 %). Additionally, the powerplant emissions penalty cost rate was also considered. Eventually, the optimal hybrid frameworks were economically and environmentally compared with a stand-alone diesel generator. Optimization results confirmed the superiority of the proposed hybrid wind-solar-biomass-battery system in comparison with other investigated systems. The cost of energy for the optimal design including biogas generator (22 kW), photovoltaic panels (30.7 kW), a 10 kW wind turbine, 11 batteries and an inverter (15.1 kW), while considering the inflation rate of 10 % and the uncertainty rate of 0 %, is equal to 0.201 $/kWh. Additionally, the selected optimal design emitted 97 % less CO2 annually, in comparison with the similarly-sized diesel-based energy system.

Evaluating the burden of illness of metabolic dysfunction-associated steatohepatitis in a large managed care population: The ETHEREAL Study.

Metabolic dysfunction-associated steatohepatitis (MASH; formerly nonalcoholic steatohepatitis) is the inflammatory form of metabolic dysfunction-associated steatotic liver disease (formerly nonalcoholic fatty liver disease). MASH is a progressive disease associated with increased risk for many hepatic and extra-hepatic complications such as cirrhosis, hepatocellular carcinoma, the requirement for liver transplantation, and cardiovascular (CV)-related and kidney-related complications. It is important to understand the clinical and economic burden of MASH. To assess and compare the clinical and economic burdens of MASH in adults with the non-MASH population in a real-world setting. This observational, retrospective study used the Healthcare Integrated Research Database (HIRD), which contains health care claims data for commercially insured and Medicare Advantage health plan members across the United States. All-cause, CV-related, and liver-related medical costs and health care resource utilization were evaluated in patients with at least 2 diagnoses of MASH during the patient identification period (October 1, 2016, to April 30, 2022) and compared with a non-MASH cohort 1:1 matched on age, Quan Charlson Comorbidity Index, region of residence, and health plan type and length of enrollment. Generalized linear regression with negative binomial and γ distribution models were used to compare health care resource utilization and medical costs, respectively, while controlling for confounders. Covariate-adjusted all-cause, CV-related, and liver-related hospitalization rate ratios and medical cost ratios were assessed and compared for the MASH and matched non-MASH cohorts. A total of 18,549 patients with MASH were compared with 18,549 matched patients in the non-MASH cohort. After adjusting for covariates, MASH was associated with significantly higher rates of hospitalization and higher medical costs compared with the non-MASH cohort. When compared with the non-MASH cohort, patients with MASH had 1.22 (95% CI = 1.15-1.30; P < 0.0001) times higher rates of all-cause hospitalization, 1.13 (95% CI = 1.03-1.24; P = 0.008) times higher rates of CV-related hospitalization, and 7.22 (95% CI = 4.91-10.61; P < 0.0001) times higher rates of liver-related hospitalization. Similarly, all-cause medical costs were 1.26 (95% CI = 1.22-1.30; P < 0.0001) times higher, CV-related medical costs were 1.66 (95% CI = 1.59-1.73; P < 0.0001) times higher, and liver-related medical costs were 7.79 (95% CI = 7.42-8.17; P < 0.0001) times higher among patients with MASH. Compared with those of the non-MASH cohort with similar age, Quan Charlson Comorbidity Index, health plan, region of residence, and duration of enrollment, patients with MASH had significantly higher all-cause, CV-related, and liver-related hospitalizations and medical costs.

Multi-variable optimization of a geothermal power plant integrated with water electrolyzer and methanol production

Renewable energy-based routes for methanol generation are recognized as essential strategies for eco-friendly energy conversion. Thus, the current study focuses on developing an innovative combined energy system for methanol production using direct carbon dioxide hydrogenation powered by geothermal energy. This is important because it addresses the need for sustainable and environmentally friendly methods of methanol production while also harnessing the potential of geothermal energy as a renewable resource. Therefore, in order to achieve multigeneration energy production, this work introduces and optimizes a novel configuration that benefits from an innovative heat design process. This configuration integrates an organic Rankine cycle, a single-flash geothermal power plant, an ammonia-water absorption chiller, a proton exchange membrane electrolyzer, and a methanol generation unit. Using the Aspen HYSYS tool, the multigeneration energy system is modeled and analyzed. As a result, the system is assessed using energy, exergetic, environmental, and economic evaluations. Furthermore, performance evaluations are carried out in the following modes: single generation, multigeneration, cooling, heat and power generation, and cooling. Furthermore, a genetic algorithm is employed to optimize the system in terms of economy and exergy. The energy and exergy efficiency are determined to be 32.57 % and 76.3 %, respectively, under the base case results. Furthermore, the rate of overall costs is 377.38 $/h. Furthermore, the precise output of carbon dioxide is −0.043 kg/kWh. The optimal unit cost and exergy efficiency for all items, taking into account the optimization scenario, are 3.34/GJ and 78.35 %, respectively. In addition, the optimum carbon dioxide and hydrogen rates for methanol generation are 92.49 mol/h and 200.51 mol/h, respectively.

Whittle index approach to the multi-class queueing systems with convex holding costs and IHR service times

AbstractWe consider the dynamic scheduling problem in a single-server multi-class queueing system, where the accumulated holding costs of a customer is a convexly increasing function of the time that the customer spends in the system. This problem was already addressed by van Mieghem in his seminal paper (Ann Appl Probab 5:808–833, 1995). He launched the so-called generalized $$c\mu $$ c μ rule and proved its asymptotic optimality in the heavy traffic limit. The optimal scheduling problem with convex holding costs have also been studied in many other papers. However, the holding costs in these papers are typically count-convex, i.e., the aggregate holding cost rate related to a class of customers increases convexly as a function of the current number of customers in this class, which is clearly different from the time-convex holding costs introduced in van Mieghem (Ann Appl Probab 5:808–833, 1995) and studied also in the present paper. We utilize the Whittle index approach to develop a novel heuristic policy for the dynamic scheduling problem with time-convex holding costs. Instead of the original open version of the continuous-time scheduling problem, we apply the Whittle index approach to the corresponding discrete-time closed version of the problem. As the main theoretical contribution, we prove that, for IHR service times, the discounted discrete-time problem is indexable and also give an explicit expression for the Whittle index itself. A special challenge in our model is the two-dimensional state space with an infinite number of possible states. By utilizing the discrete-time results, we develop a novel index policy for the original continuous-time problem and demonstrate, by numerical simulations, that this Whittle index policy outperforms the generalized $$c\mu $$ c μ rule. Thus, while the generalized $$c\mu $$ c μ rule is asymptotically optimal in the heavy traffic limit, it can still be improved in normal traffic conditions.

Drift Control of High-Dimensional Reflected Brownian Motion: A Computational Method Based on Neural Networks

Motivated by applications in queueing theory, we consider a stochastic control problem whose state space is the d-dimensional positive orthant. The controlled process Z evolves as a reflected Brownian motion whose covariance matrix is exogenously specified, as are its directions of reflection from the orthant’s boundary surfaces. A system manager chooses a drift vector [Formula: see text] at each time t based on the history of Z, and the cost rate at time t depends on both [Formula: see text] and [Formula: see text]. In our initial problem formulation, the objective is to minimize expected discounted cost over an infinite planning horizon, after which we treat the corresponding ergodic control problem. Extending the earlier work by Han et al. [Han J, Jentzen A, Weinan E (2018) Solving high-dimensional partial differential equations using deep learning. Proc. Natl. Acad. Sci. USA 115(34):8505–8510], we develop and illustrate a simulation-based computational method that relies heavily on deep neural network technology. For the test problems studied thus far, our method is accurate to within a fraction of 1% and is computationally feasible in dimensions up to at least [Formula: see text].

Grouping-maintenance of multi-components production system under ecological background

Purpose: The purpose of this paper is to propose an opportunistic group maintenance model for a multi-component series system considering reducing CO2 emission and increasing system efficiency, which establishes a maintenance policy to minimize the cost rate of the system life cycle.Design/methodology/approach: Structural dependence and economic dependence between the components in a multi-component series system are analyzed to make a condition-based maintenance policy. To minimize the cost rate of the system life cycle, clustering theory and two decision variables, which include the preventative maintenance cycle multiplier and basic preventive maintenance interval of each component, is utilized to make an opportunistic policy for system optimal maintenance under the background of increasing energy efficiency and reducing CO2 emissions.Findings: It can be concluded that the government imposes fines on excessive CO2 emissions and energy consumption indicators, which can influence the maintenance decisions of enterprises, promote enterprises to shorten the cycle of preventive maintenance, and avoid excessive CO2 emissions of various components and exceed the indicators as much as possible, so as to enable enterprises to actively save energy, reduce emissions and control carbon emissions. Furthermore, when the single preparation cost of repair maintenance increases, enterprises need to shorten the maintenance period to avoid frequent component failures. As the cost of a single prep for preventive maintenance rises, organizations need to extend maintenance cycles and avoid frequent parts downtime - minimizing their own repair costs.Practical implications: Considering the high maintenance cost and low energy efficiency of multi-component systems, this model assists production managers to have better maintenance of these systems.Originality/value: 1.Model innovation: comprehensive consideration of two variables in the selection of decision variables for preventive maintenance or opportunistic maintenance; The selection and synthesis of ecological factors when making ecologically conscious maintenance decisions for multi-component systems make up for the gaps of single variables and one-sided indicators in previous studies and models. 2. Methodological innovation: In the identification of opportunity maintenance, the idea of clustering is first combined; In the simulation analysis, genetic algorithm is used to obtain the optimal parameters quickly and accurately. A blend of management, statistics, biology and computer science.

Load frequency control performance enhancement of an electric vehicle integrated time-delay multi-microgrid system

ABSTRACT Frequency control in a microgrid (μG) is problematic due to low inertia of distributed generators (DGs) and uncertainty of the renewable energy source-dependent DGs. Consequently, deploying distributed storage units (DSUs) in a μG is crucial for quickly arresting frequency deviations. The electric vehicle (EV) technology, as DSU, is being preferred over conventional energy storage due to its lower cost and slower degradation rate, facilitating demand side response in a μG. The adoption of the EV technology necessitates an open communication infrastructure in the μG, with a pre-existing communication time delay (CTD). This article explores the impact of EV integration on load frequency control (LFC) performance of a multi-microgrid (MμG) system and determines an optimal CTD value simultaneously. A cyclical parthenogenesis algorithm-optimized 2DOF-FOPID controller is implemented to obtain dynamic responses of the MμG system. Simulation results reveal significant improvements in dynamic responses when integrating EVs in the MμG, with a 97.33% increase in objective function value. The recommended controller also outperforms standard controllers in terms of how quickly frequency and tie-line power variations settle down to zero. Thus, the proposed controller meets the LFC requirements. Robustness of the proposed LFC scheme is tested against parametric variations in the MμG system.

Optimizing solar photovoltaic farm-based cogeneration systems with artificial intelligence (AI) and Cascade compressed air energy storage for stable power generation and peak shaving: A Japan-focused case study

This study proposes a novel solar cogeneration system that integrates compressed air energy storage units (CAES) and gas turbines (GT) with a solar farm consisting of photovoltaic panels. The primary objective of this research is to address the instability of solar energy production and help during peak energy consumption by utilizing CAES. The proposed system is modeled using EES software, and its performance is optimized using advanced artificial intelligence (AI) methods, including artificial neural networks and intelligent algorithms. The analysis identifies five critical decision variables that significantly impact system performance: the number of photovoltaic panels, CAES pressure ratio, CAES inlet pressure, gas turbine efficiency, and compressor efficiency. The results demonstrate that the optimized solar cogeneration system can achieve exergy efficiency of 36.44 % and a cost rate of 13.76 $/hour. The exergy analysis of the system indicates that the most significant destruction is related to the solar farm, gas turbine, and compressors. Furthermore, this study investigates the effect of weather in eight Japanese cities on system performance, considering two operating modes: with and without using system electricity (PV mode). The results show that the proposed solar cogeneration system has significant potential for clean electricity generation and CAES applications to overcome the instability of the solar system and help during peak energy consumption throughout the year. The study's findings highlight the attractive potential of integrating CAES and AI technologies in solar photovoltaic systems for stable and efficient power generation in Japan.

359 Optimization of calcium inclusion levels in diets fed to growing and finishing beef cattle

Abstract Calcium (Ca) is a mineral that has an essential role in many biochemical pathways and functions of beef cattle. Limestone is the primary supplemental source of Ca in diets fed to growing and finishing cattle in the United States. Preliminary data indicated that inclusion of dietary Ca above the NASEM (2016) requirements as well as mean and modal concentrations reported by the most recent survey of consulting nutritionists may improve average daily gain (ADG) and feed efficiency (gain to feed ration; G:F) of finishing beef cattle. Thus, the objective of this experiment was to determine the optimal concentration of Ca in growing and finishing diets when limestone was the supplemental source of added Ca. A database was constructed containing 89 treatment means from 23 experiments published from 1963 to 2024 where dietary Ca inclusion via limestone represented treatments. Search parameters to construct the database included a combination of Ca, limestone, performance, efficiency, ADG, G:F, feedlot cattle, finishing cattle, growing cattle, and backgrounding cattle. Data were analyzed in JMP Pro 17 using a mixed model to determine linear and quadratic relationships between dietary Ca and ADG, DMI, or G:F with citation as a random effect. An economic sensitivity analysis was conducted to determine variables most influential to the dietary Ca concentration projected to minimize cost of gain using the above models. Concentration of dietary calcium was selected as the explanatory variable for the models rather than limestone because a greater proportion of variation was accounted for in the response variables prior to the citation adjustment (Ca R2 0.05 to 0.25; limestone R2 0.06 to 0.21). Diet Ca concentration and DMI were unrelated (P ≥ 0.75), but diet Ca was related to both ADG (R2 = 0.84; RMSE = 0.36; P ≤ 0.03) and G:F (R2 = 0.74; RMSE = 0.01; P &amp;lt; 0.01) in a quadratic fashion. Maximum ADG occurred when Ca was included at 1.20% of diet DM; approximately 2.6-fold greater than Ca requirements estimated by NASEM (2016). Cost of gain was minimized when Ca was 1.35% of diet DM. Sensitivity analysis revealed Ca concentration to minimize cost of gain was sensitive to changes in DMI, ADG, days on feed, ration cost and cost of yardage. These results provide further evidence that it may be beneficial to feed a greater amount of Ca than currently recommended by the NASEM (2016) for growing and finishing cattle. Future pen-scale feeding trials are warranted to validate the results of this meta-analysis and to determine the dose response of cattle fed graded levels of Ca. Future research should also be conducted to determine the mechanism of action responsible for the observed improvements in animal performance and efficiency resulting from feeding an increased amount of dietary Ca.

Optimal 4E design and innovative R-curve approach for a gas-solar- biological waste polygeneration system for power, freshwater, and methanol production

BackgroundCogeneration power plants traditionally rely on fossil fuels to produce stable power and heat. However, increasing energy demand and population growth have intensified the emission of biological pollutants due to fossil fuel use. The Global Alliance on Health and Pollution advocates for integrating renewable energy sources to mitigate these issues. ObjectivesThis study aims to evaluate the integration of a solar-biomass polygeneration system with a hybrid solar-waste-fossil fuel cogeneration system. The goal is to analyze the system from technical, economic, and environmental perspectives, focusing on optimizing energy demand and minimizing environmental impact. MethodsTo assess energy demand and supply, the R-curve methodology was applied to the hybrid cogeneration system, with a specific focus on solar and biomass renewable energies. Various scenarios were analyzed, including total annual costs, pollutant emissions, water footprint, and overall environmental impact based on life cycle assessment. The study examined and compared the performance of three types of biomass waste (Municipal solid waste, mixed paper waste, and date palm waste). Multi-objective optimization was performed using artificial intelligence and machine learning techniques, employing four meta-heuristic algorithms. The conditions generated by each algorithm were analyzed and compared. ResultsMunicipal solid waste, being the most readily available fuel, provided the most favorable economic conditions for the system. Environmentally, municipal solid waste ranked in the middle compared to other fuels. Among the optimization algorithms, the Salps swarm algorithm proved to be the most efficient in terms of calculation time and system efficiency improvements. The optimization improved net power generation by 5.25 %, overall energy efficiency by 16.27 %, total cost rate by 10.19 %, and total environmental impact rate by 14.02 %. ConclusionThe integrated system's performance was analyzed across different climatic change throughout the year. The multi-objective Salps swarm algorithm optimization demonstrated significant benefits in enhancing system efficiency and reducing costs and environmental impacts.

Optimizing trigeneration energy systems: Biogas-centric methanol production via direct CO2 hydrogenation with advanced integration of PEM electrolyzer and LNG cold technology

Biogas, a sustainable alternative to fossil fuels, addresses issues of non-renewability and accessibility. Its structural similarity to fossil fuels makes it a potent option for energy systems. With this in mind, this paper discusses a novel trigeneration system that utilizes biogas and Liquefied natural gas cooling to produce methanol, electricity, cold water, hot water, oxygen, and natural gas. The system integrates various components such as a biogas burner, Kalina cycle, organic Rankine cycle, liquefied natural gas liquid gasification cycle, proton exchange membrane electrolyzer, and methanol synthesis unit. Simulation via Aspen HYSYS software includes an analysis of energy, exergy, economic, and environmental aspects. Efficiency assessment in single generation, cogeneration, trigeneration, and chemical trigeneration modes concludes chemical trigeneration as most efficient, with the proton exchange membrane electrolyzer being the most efficient subsystem. Key variables like Kalina cycle evaporator temperature, gas flow rate to the methanol reactor, and organic Rankine cycle working fluid pressure are assessed. Predictions on thermodynamic, environmental, and economic behaviors, along with their fluctuations, are made. Using a thermoeconomic approach, the economic analysis determines an exergy unit cost of 59.79 $/GJ and a total cost rate of 2764 $/h. Overall, this work presents a novel and efficient chemical trigeneration system that utilizes biogas and LNG cooling to produce multiple products.

Optimized multi-criteria performance of a poly-generation layout including a Stirling engine and a supercritical Brayton cycle using biogas and methane as two potential fuels of a topping gas turbine cycle

The combination of a supercritical Brayton cycle, an absorption chiller, a Stirling engine, a reverse osmosis desalination system, and a proton exchange membrane electrolyzer is investigated for waste heat recovery of a topping gas turbine cycle. The required electricity for hydrogen and freshwater production is supplied by the Stirling engine and supercritical Brayton cycle, respectively. In addition, the output power is provided by the gas turbine cycle, and the exiting cold stream of the Stirling engine is considered hot water for domestic utilization. A comparison is made between biogas and pure methane as two possible fuels for the system. Energy and exergy analyses, economic analysis using the specific exergy costing approach, and environmental analysis considering the carbon dioxide emissions are employed in the study. The three-objective optimization of the configuration discloses an exergy efficiency (ηex) of 39.49 % and 39.85 % in the cases of biogas and methane, respectively. The proposed system can improve ηex by 6.18 % points compared to the stand-alone GTC. The specific cost of poly-generation (cpoly) and the total cost rate (C˙tot) are obtained as 25.92 $GJ−1 and 249.5 $h−1 in the biogas mode, while these values are calculated as 36.75 $GJ−1 and 336.7 $h−1 in the methane case. The environmental cost rate (C˙env) of the system is 24.78 $h−1 in the case of biogas and 17.47 $h−1 in the methane mode. The results confirm the superiority of the methane case from the environmental viewpoint over biogas. However, the low values of cpoly and C˙tot in the biogas case indicate that biogas is superior to methane from a general standpoint. The utilization of the present setup for waste heat recovery of biogas-driven GTCs is recommended due to the higher ηex than the previous layouts.

Hybrid group opportunistic maintenance strategy for offshore wind turbines considering maintenance resource allocation in harsh working environments

The intelligent maintenance of offshore wind turbines (OWTs) is vital for sustainable energy generation and utilization, as harsh working environments generally limit their application. As offshore wind farms expand into deeper waters, operational and maintenance costs continue to increase. Therefore, devising targeted maintenance strategies for offshore wind farms is crucial. Existing strategies often overlook the role of maintenance resources in controlling the operational and maintenance costs. To address this problem, this study proposes a hybrid group opportunistic maintenance strategy to implement resource allocation-based maintenance for OWTs under dynamic conditions. First, a failure rate model based on the life decline and system coupling is proposed to capture the evolution of failures in OWTs in harsh working environments, thereby enhancing the accuracy of fault maintenance. Subsequently, a hybrid linear and nonlinear deterioration model is proposed to identify the different deterioration trends owing to the various sensitivities of diverse components to harsh working environments, which enhances the precision of degradation maintenance. In addition, a maintenance resource allocation model is proposed to allocate resources in advance, thereby increasing resource utilization and reducing maintenance costs. A multi-objective optimization approach is proposed to maximize the mean maintenance level (MML) and minimize the per unit cycle expected maintenance cost rate (PECR) by considering harsh working environments. Finally, the nutcracker optimization algorithm is introduced into the maintenance model to generate the optimal solution in a multi-objective manner. The results show that the maintenance model is feasible and effective for practical adaptive maintenance applications of OWTs in harsh working environments in practical applications.

Cost-based performance optimization of a single system under a hierarchical imperfect maintenance policy

Abstract Due to the influence from external factors such as repair sites and seasonal climate change, it is difficult to restore the system performance to any intermediate level between perfect maintenance and minimal maintenance through imperfect maintenance. This article first categorizes the states of the system just before repair into three classes based on its internal degradation level: failure, major defect, and minor defect, with three corresponding thresholds. Subsequently, the corresponding repairs are carried out to dwindle the system’s degradation to different levels. In detail, if the internal degradation level of the system just before repair is recognized as minor defect, an imperfect repair, termed as type I imperfect repair, is implemented to scale down the degradation level below the minor defect threshold. If the degradation level is identified as major defect, an upgraded imperfect repair, termed as type II imperfect repair, is executed to only lower the degradation to the level between the minor defect threshold and major defect threshold. Otherwise, if the degradation before repair is beyond the failure threshold, replacement will be carried out instead of these imperfect repairs. Thus, a novel hierarchical imperfect maintenance structure is introduced. Then, a multi-variable repair cost model is constructed when considering the related costs incurred from inspection, type I imperfect repair, type II imperfect repair, replacement, and even system downtime. Finally, with the aid of the stationary law of Markov chains and the semi-regenerative process, the cost-based performance optimization with three parameters, including the inspection interval, minor defect threshold, and major defect threshold, is explored through a numerical experiment, and the closed expression of the optimal cost rate is provided.

A novel approach for perfusion process design based on a "Grey-Box" kinetic model.

Perfusion cell-culture mode has caught industrial interest in the field of biomanufacturing in recent years. Thanks to new technology, perfusion-culture processes can support higher cell densities, higher productivities and longer process times. However, due to the inherent operational complexity and high running costs, the development and design of perfusion-culture processes remain challenging. Here, we present a model-based approach to design optimized perfusion cultures of Chinese Hamster Ovary cells. Initially, four batches of bench-top reactor continuous-perfusion-culture data were used to fit the model parameters. Then, we proposed the model-based process design approach, aiming to quickly find out the "theoretically optimal" operational parameters combinations (perfusion rate and the proportion of feed medium in perfusion medium) which could achieve the target steady-state VCD while minimizing both medium cost and perfusion rate during steady state. Meanwhile, we proposed a model-based dynamic operational parameters-adjustment strategy to address the issue of cell-growth inhibition due to the high osmolality of concentrated perfusion medium. In addition, we employed a dynamic feedback control method to aid this strategy in preventing potential nutrient depletion scenarios. Finally, we test the feasibility of the model-based process design approach in both shake flask semi-perfusion culture (targeted at 5 × 107 cells/ml) and bench-top reactor continuous perfusion culture (targeted at 1.1 × 108 cells/ml). This approach significantly reduces the number of experiments needed for process design and development, thereby accelerating the advancement of perfusion-mode cell-culture processes.

A novel geothermal system combined with fuel cell and hydrogen generation to store clean sustainable energy storage

This paper presents a thermo-economic assessment of a novel geothermal integrated system enhanced by a fuel cell. The proposed system leverages the advantages of geothermal energy resources, featuring a sustainable and environmentally friendly solution for power generation and district heating applications. Integrating a fuel cell and thermoelectric within the geothermal system aims to improve overall system performance and energy efficiency by converting waste heat into electricity. A detailed mathematical model of the integrated system is developed, incorporating various performance parameters, component efficiencies, and thermo-economic considerations. The model is applied to simulate the system's performance under different operating conditions and parameters. Additionally, sensitivity analyses are conducted to evaluate the effects of key design variables on the system's performance, cost, and environmental impacts. The results indicate that the newly developed system generates 95594 kW of electricity with a total exergy destruction rate of 4616 kW. The electricity cost rate also obtained 0.309 $/kWh. The energy efficiency of the introduced system is 24.08 % and the exergy efficiency is 26.7 %. Comparison with the basic system represents that energy efficiency shows a 15.71 % improvement.