Digital twin-based analysis of volumetric error mapping procedures

The evaluation of the volumetric accuracy of a machine tool is an open challenge in the industry, and a wide variety of technical solutions are available in the market and at research level. All solutions have advantages and disadvantages concerning which errors can be measured, the achievable uncertainty, the ease of implementation, possibility of machine integration and automation, the equipment cost and the machine occupation time, and it is not always straightforward which option to choose for each application. The need to ensure accuracy during the whole lifetime of the machine and the availability of monitoring systems developed following the Industry 4.0 trend are pushing the development of measurement systems that can be integrated in the machine to perform semi-automatic verification procedures that can be performed frequently by the machine user to monitor the condition of the machine. Calibrated artefact based calibration and verification solutions have an advantage in this field over laser based solutions in terms of cost and feasibility of machine integration, but they need to be optimized for each machine and customer requirements to achieve the required calibration uncertainty and minimize machine occupation time.This paper introduces a digital twin-based methodology to simulate all relevant effects in an artefact-based machine tool calibration procedure, from the machine itself with its expected error ranges, to the artefact geometry and uncertainty, artefact positions in the workspace, probe uncertainty, compensation model, etc. By parameterizing all relevant variables in the design of the calibration procedure, this simulation methodology can be used to analyse the effect of each design variable on the error mapping uncertainty, which is of great help in adapting the procedure to each specific machine and user requirements. The simulation methodology and the analysis possibilities are illustrated by applying it on a 3-axis milling machine tool.     

Hybrid hydrate processes for CO2/H2 mixture purification: A techno-economic analysis

In this work, hydrate based separation technique was combined with membrane separation and amine-absorption separation technologies to design hybrid processes for separation of CO₂/H₂ mixture. Hybrid processes are designed in the presence of different types of hydrate promoters. The conceptual processes have been developed using Aspen HYSYS. Proposed processes were simulated at different flow rates for the feed stream. A comprehensive cost model was developed for economic analysis of novel processes proposed in this study. Based on the results from process simulation and equipment sizing, the amount of total energy consumption, fixed cost, variable cost, and total cost were calculated per unit weight of captured CO₂ for various flow rates of feed stream and in the presence of different hydrate promoters. Results showed that combination of hydrate formation separation technique with membrane separation technology results in a CO₂ capture process with lowest energy consumption and total cost per unit weight of captured CO₂. As split fraction and heat of hydrate formation increases, the share of hydrate formation section in total energy consumption increases. When TBAB is applied as hydrate promoter, due to its higher hydrate separation efficiency, more amount of CO₂ is captured in hydrate formation section and consequently the total cost for process decreases considerably. Hybrid hydrate-membrane process in the presence of TBAB as hydrate promoter with 29.47 US$/ton CO₂ total cost is the best scheme for hybrid hydrate CO₂ capture process. Total cost for this process is lower than total cost for single MDEA-based absorption process as the mature technology for CO₂ capture.     

An economic investigation of the wind-hydrogen projects: A case study

Hydrogen as an energy carrier can play a significant role in reducing environmental emissions if it is produced from renewable energy resources. This research aims to assess hydrogen production from wind energy considering environmental, economic, and technical aspect for the East Azerbaijan province of Iran. The economic assessment is performed by calculation of payback period, levelized cost of hydrogen, and levelized cost of electricity. Since uncertainty in the power output of wind turbines may affect the payback period, all calculations are performed for four different turbine degradation rates. While it is common in the literature to choose the wind turbine based on a single criterion, this study implements Multi-Criteria Decision-Making (MCDM) techniques for this purpose. The results of Step-wise Weight Assessment Ratio Analysis illustrates that economic issue is the most important criterion for this research. The results of Weighted Aggregated Sum Product Assessment shows that Vestas V52 is the most suitable wind turbine for Ahar and Sarab cities, while Eovent EVA120 H-Darrieus is a better choice for other stations. The most suitable location for wind power generation is found to be Ahar, where it is estimated to annually generate 2914.8 kWh of electricity at the price of 0.045 $/kWh, and 47.2 tons of hydrogen at the price of 1.38 $/kg, which result in 583 tons of CO₂ emission reduction.     

A strategic roadmap for large-scale green hydrogen demonstration and commercialisation in China: A review and survey analysis

Hydrogen is gaining increased attention from industries and policymakers in China. However, most of the current demonstration projects in the country have relied on conventional energy sources, including industrial byproduct hydrogen and grey hydrogen produced from fossil fuels. Moreover, strategies and policy frameworks leading to a shift to green or low-carbon hydrogen have neither been explored in-depth nor been identified clearly in the context of China. This study aims at bridging such gaps. Roadmapping techniques enhanced by the Delphi method and SWOT analysis are used to survey hydrogen energy experts from government bodies, industries, and academia to achieve basic agreement on strategically enabling large-scale green hydrogen demonstrations followed by commercialisation in China. The outcome of two rounds of surveys showed that experts' opinions converged on a strategic roadmap with three stages of development. The corresponding policies needed in each stage are evaluated and selected to form a systemic framework.     

Psychrophilic hydrogen production from petrochemical wastewater via anaerobic sequencing batch reactor: techno-economic assessment and kinetic modelling

Independent hydrogen production from petrochemical wastewater containing mono-ethylene glycol (MEG) via anaerobic sequencing batch reactor (ASBR) was extensively assessed under psychrophilic conditions (15–25 °C). A lab-scale ASBR was operated at pH of 5.50, and different organic loading rates (OLR) of 1.00, 1.67, 2.67, and 4.00 gCOD/L/d. The hydrogen yield (HY) progressed from 134.32 ± 10.79 to 189.09 ± 22.35 mL/gMEG_initial at increasing OLR from 1.00 to 4.00 gCOD/L/d. The maximum hydrogen content of 47.44 ± 3.60% was achieved at OLR of 4.0 gCOD/L/d, while methane content remained low (17.76 ± 1.27% at OLR of 1.0 gCOD/L/d). Kinetic studies using four different mathematical models were conducted to describe the ASBR performance. Furthermore, two batch-mode experiments were performed to optimize the nitrogen supplementation as a nutrient (C/N ratio), and assess the impact of salinity (as gNaCl/L) on hydrogen production. HY substantially dropped from 62.77 ± 4.09 to 6.02 ± 0.39 mL/gMEG_initial when C/N ratio was increased from 28.5 to 114.0. Besides, the results revealed that salinity up to 10.0 gNaCl/L has a relatively low inhibitory impact on hydrogen production. Eventually, the cost/benefit analysis showed that environmental and energy recovery revenues from ASBR were optimized at OLR of 4.0 gCOD/L/d (payback period of 7.13 yrs).     

Stochastic free vibration analyses of composite shallow doubly curved shells – A Kriging model approach

This paper presents the Kriging model approach for stochastic free vibration analysis of composite shallow doubly curved shells. The finite element formulation is carried out considering rotary inertia and transverse shear deformation based on Mindlin’s theory. The stochastic natural frequencies are expressed in terms of Kriging surrogate models. The influence of random variation of different input parameters on the output natural frequencies is addressed. The sampling size and computational cost is reduced by employing the present method compared to direct Monte Carlo simulation. The convergence studies and error analysis are carried out to ensure the accuracy of present approach. The stochastic mode shapes and frequency response function are also depicted for a typical laminate configuration. Statistical analysis is presented to illustrate the results using Kriging model and its performance.     

A GIS plug-in for Bayesian belief networks: Towards a transparent software framework to assess and visualise uncertainties in ecosystem service mapping

The complexity and spatial heterogeneity of ecosystem processes driving ecosystem service delivery require spatially explicit models that take into account the different parameters affecting those processes. Current attempts to model ecosystem service delivery on a broad, regional scale often depend on indicator-based approaches that are generally not able to fully capture the complexity of ecosystem processes. Moreover, they do not allow quantification of uncertainty on their predictions. In this paper, we discuss a QGIS plug-in which promotes the use of Bayesian belief networks for regional modelling and mapping of ecosystem service delivery and associated uncertainties. Different types of specific Bayesian belief network output maps, delivered by the plug-in, are discussed and their decision support capacities are evaluated. This plug-in, used in combination with firmly developed Bayesian belief networks, has the potential to add value to current spatial ecosystem service accounting methods. The plug-in can also be used in other research domains dealing with spatial data and uncertainty.     

Economic growth,CO2 emissions,and energy consumption in the five ASEAN countries

This paper investigates the relationship between economic growth, carbon dioxide (CO₂) emissions, and energy consumption with an aim to test the validity of the Environmental Kuznets Curve (EKC) hypothesis in five ASEAN (Association of South East Asian Nations) countries (Indonesia, Malaysia, Philippines, Singapore, and Thailand) by applying the panel smooth transition regression (PSTR) model as a new econometric technique. The PSTR model is more flexible and appropriate for describing cross-country heterogeneity and time instability. Our empirical results strongly rejected the null hypothesis of linearity, and the test for no remaining nonlinearity indicated a model with one transition function and two threshold parameters. The first regime (levels of GDP per capita below 4686 USD) showed that environmental degradation increases with economic growth while the trend was reversed in the second regime (GDP per capita above 4686 USD). The results also showed that energy consumption with either the first or the second regime lead to increase CO₂. The overall results support the validity of the EKC hypothesis in the ASEAN countries.     

Generation expansion planning in electricity markets: A novel framework based on dynamic stochastic MPEC

This paper presents a novel framework for generation expansion planning (GEP) of restructured power systems under uncertainty in a multi-period horizon, which includes generation investment from a price maker perspective. The investment problem is modeled as a bi-level optimization problem. The first level problem includes decisions related to investment in order to maximize total profit in the planning horizon. The second level problem consists of maximizing social welfare where the power market is cleared. Rival uncertainties on offering and investment are modeled using sets of scenarios. The bi-level optimization problem is then converted to a dynamic stochastic MPEC and represented as a mixed integer linear program (MILP) after linearization. The proposed framework is examined on a typical six-bus power network, MAZANDARAN regional electric company (MREC) transmission network as an area of IRAN interconnected power system and IEEE RTS 24-bus network. Simulation results confirm that the proposed framework can be a useful tool for analyzing the behavior of investments in electricity markets.