Monte Carlo Sensitivity Analysis Research Articles

Developing a Quantitative Methods Course for Civil and Construction Engineering Students

The ability to apply quantitative methods to gather, review, analyze and draw conclusions from data to support decision making is an important skillset in the engineering profession. We developed a new three-credit hour undergraduate course to develop a deeper understanding and appreciation for basic quantitative methods for construction engineering students. The course’s main objective is to provide basic skills in quantitative methods, by familiarizing students with the critical steps in an analytical approach to decision-making. The course applies a hands-on approach to problem solving and decision making using various engineering-themed problems that practicing engineers are likely to face. The specific methods explored include; constructing quantitative models to include break-even analysis, decision trees, linear optimization, PERT/CPM schedule development and crashing, as well as Monte Carlo Simulation and sensitivity analysis to generate and interpret problem solutions. Instruction emphasizes a hands-on approach and practical assignments to develop a logical framework for describing and solving problems. This new course emphasizes Excel software rather than hand calculations and introduces use of Excel Solver and other data analysis tools.

Modeling and simulation of optical system error transmission in the laser tracker

The optical system of the laser tracker utilizes plane mirrors to construct a reflective path, reducing its size and weight. However, maintaining the alignment of the laser with the ideal optical axis during its propagation in the optical system poses significant challenges in the design, fabrication, and assembly of the optical system. This paper explores the principle of error propagation during the assembly process of the optical system and improves the accuracy of the output laser through a numerical simulation and optimization methods. A general error model for the optical system is established to understand the principle of error propagation. A Monte Carlo numerical simulation and sensitivity analysis are used to study the influence of various errors on the accuracy of the output laser. The machining errors are optimized using a simulated annealing method to balance the manufacturing difficulty and system accuracy. The assembly process is also optimized to reduce the degrees of freedom and the number of optical parts required, and verified by experiments. The experimental results indicate that the average position error of the output laser is 15.743 µm, and the average angle error is 1.427′′. This study provides what we believe is a novel approach and methodology for the design and alignment of optical systems.

Onboard carbon capture and storage (OCCS) for fossil fuel-based shipping: A sustainability assessment

Limiting the carbon intensity of maritime transport is crucial to meet 2050 net-zero targets. Onboard carbon capture and storage (OCCS) offers a practical short-term solution for reducing shipping-related CO2 emissions until cleaner technologies are ready for large-scale adoption. This study introduces an innovative multi-objective approach to integrate sustainability into the conceptual design and decision-making phases of OCCS. A systematic technology screening identified possible OCCS solutions, which were then assessed for onboard feasibility. Specific indicators were defined to evaluate OCCS performance based on technological, economic, environmental, and social criteria, and aggregated sustainability perspectives. Using a fossil fuel-powered cruise ship as a case study, results were benchmarked against zero-carbon alternatives. Among the alternatives considered, chemical absorption by amine scrubbing (AS) and advanced cryogenic carbon capture (A3C) appeared as the only feasible solutions considering onboard energy requirements. The emerging cryogenic A3C concept resulted in being outperformed by benchmark AS, primarily due to an environmental impact 1.5 times higher. All alternative technologies, whether OCCS- or cleaner fuel-based, were found to be more sustainable than the baseline fossil fuel-based engine, lowering the environmental impact by at least 61%. Hydrogen as a marine fuel leads to the most promising scenario for future cleaner shipping operations, reducing the sustainability footprint by up to 76%. The robustness of the proposed method was confirmed by a probabilistic Monte Carlo sensitivity analysis. Overall, the results obtained can guide toward more informed solutions and policies promoting the sustainability of ship propulsion systems.

Traversing the land-sea interface: A climate change risk assessment of terrestrially breeding marine predators.

Terrestrially breeding marine predators have experienced shifts in species distribution, prey availability, breeding phenology, and population dynamics due to climate change worldwide. These central-place foragers are restricted within proximity of their breeding colonies during the breeding season, making them highly susceptible to any changes in both marine and terrestrial environments. While ecologists have developed risk assessments to evaluate climate risk in various contexts, these often overlook critical breeding biology data. To address this knowledge gap, we developed a trait-based risk assessment framework, focusing on the breeding season and applying it to marine predators breeding in parts of Australian territory and Antarctica. Our objectives were to quantify climate change risk, identify specific threats, and establish an adaptable assessment framework. The assessment considered 25 criteria related to three risk components: vulnerability, exposure, and hazard, while accounting for uncertainty. We employed a scoring system that integrated a systematic literature review and expert elicitation for the hazard criteria. Monte Carlo sensitivity analysis was conducted to identify key factors contributing to overall risk. We identified shy albatross (Thalassarche cauta), southern rockhopper penguins (Eudyptes chrysocome), Australian fur seals (Arctocephalus pusillus doriferus), and Australian sea lions (Neophoca cinerea) with high climate urgency. Species breeding in lower latitudes, as well as certain eared seal, albatross, and penguin species, were particularly at risk. Hazard and exposure explained the most variation in relative risk, outweighing vulnerability. Key climate hazards affecting most species include extreme weather events, changes in habitat suitability, and prey availability. We emphasise the need for further research, focusing on at-risk species, and filling knowledge gaps (less-studied hazards, and/or species) to provide a more accurate and robust climate change risk assessment. Our findings offer valuable insights for conservation efforts, given that monitoring and implementing climate adaptation strategies for land-dependent marine predators is more feasible during their breeding season.

Monte Carlo Simulation, Artificial Intelligence and Machine Learning-based Modelling and Optimization of Three-dimensional Electrochemical Treatment of Xenobiotic Dye Wastewater

The present study investigates the synergistic performance of the three-dimensional electrochemical process to decolourise methyl orange (MO) dye pollutant from xenobiotic textile wastewater. The textile dye was treated using electrochemical technique with strong oxidizing potential, and additional adsorption technology was employed to effectively remove dye pollutants from wastewater. Approximately 98% of MO removal efficiency was achieved using 15 mA/cm2 of current density, 3.62 kWh/kg of energy consumption and 79.53% of current efficiency. The 50 mg/L MO pollutant was rapidly mineralized with a half-life of 4.66 min at a current density of 15 mA/cm2. Additionally, graphite intercalation compound (GIC) was electrically polarized in the three-dimensional electrochemical reactor to enhance the direct electrooxidation and.OH generation, thereby improving synergistic treatment efficiency. Decolourisation of MO-polluted wastewater was optimized by artificial intelligence (AI) and machine learning (ML) techniques such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and random forest (RF) algorithms. Statistical metrics indicated the superiority of the model followed this order: ANN > RF > SVM > Multiple regression. The optimization results of the process parameters by artificial neural network (ANN) and random forest (RF) approaches showed that a current density of 15 mA/cm2, electrolysis time of 30 min and initial MO concentration of 50 mg/L were the best operating parameters to maintain current and energy efficiencies of the electrochemical reactor. Finally, Monte Carlo simulations and sensitivity analysis showed that ANN yielded the best prediction efficiency with the lowest uncertainty and variability level, whereas the predictive outcome of random forest was slightly better.Highlights• In-depth analysis of various artificial intelligence optimization techniques.• Prediction efficiency of artificial intelligence and machine learning algorithms.• 98% dye removal and 100% regeneration of graphite intercalation compound.• Advanced statistical analysis of targeted responses and data fitting techniques.• Analysis of uncertainties and variability using Monte Carlo simulation.

Sustainable jet fuel from municipal solid waste–Investigation of carbon negativity and affordability claims

Addressing the challenges posed by municipal solid waste (MSW) and alternative aviation fuel is crucial for promoting circular economy and achieving carbon neutrality aligned with UN Sustainable Development Goals. While most studies in this field have focused on catalyst development for MSW-to-jet fuel conversion, this study investigates the environmental and economic performance of sustainable aviation fuels derived from MSW. Life cycle emissions for six conversion pathways were estimated to be 1187–9694 kg CO2 eq/t jet fuel. Fischer-Tropsch (FT) and Plasma FT synthesis emerged as promising options due to their lower greenhouse gas emissions (∼2000 kg CO2eq/t jet fuel) and high net present value (∼$859 million). Furthermore, Monte Carlo simulations and sensitivity analysis quantified the potential outcomes for net present value and carbon footprints. This study also discussed social acceptance, policy implications and real-world limitations for strategic decision-making and advancing the transition to alternative aviation fuels.

Potential Cost Savings with 60-day Peripheral Nerve Stimulation Treatment in Chronic Axial Low Back Pain.

Chronic axial low back pain (CLBP) that is not responsive to medication management or physical therapy often requires significant clinical intervention. Several interventional pain management options exist, including a 60-day peripheral nerve stimulation (PNS) treatment. This economic evaluation investigated the potential for projected cost savings associated with prioritizing 60-day PNS treatment relative to a 'standard of care' (SOC) approach (where patients do not have access to 60-day PNS). A decision tree (supervised machine learning) model tracked treatment progression across two hypothetical cohorts of US patients with CLBP in whom non-interventional options were ineffective (Cohort A: treatment starting with 60-day PNS followed by any additional interventional and surgical treatments versus Cohort B: standard of care interventional and surgical treatments without access to 60-day PNS). Treatment efficacy estimates were based on published success rates. Conditional on treatment failure, up to two additional interventions were considered within the 12-month time frame in both cohorts. SOC treatment options included epidural injection, radiofrequency ablation (RFA), basivertebral nerve ablation (BVNA), PNS permanent implant (PNS-PI), spinal cord stimulator (SCS) trial/implant, and spinal fusion surgery. Treatment choice probabilities in both cohort algorithms were based on clinician interviews. Costs were based on national Medicare reimbursement levels in the ambulatory surgery center (ASC) setting. Savings reflected the difference in projected costs between cohorts. A Monte Carlo simulation and sensitivity analyses were conducted to generate confidence intervals and identify important inputs. The treatment algorithm which prioritized initial 60-day PNS treatment was projected to save $8056 (95% CI $6112-$9981) per patient during the first year of interventional treatment relative to the SOC approach. Use of the 60-day PNS treatment as an initial interventional treatment in patients with CLBP may result in significant savings for Medicare. Projected savings may be even larger for commercial payers covering non-Medicare patients.

Critical component detection in assemblies: a graph centrality approach

AbstractThis study examines the use of graph centrality to identify critical components in assembly models, a method typically dominated by computationally intense analyses. By applying centrality measures to simulated assembly graphs, components were ranked to assess their criticality. These rankings were compared against Monte Carlo sensitivity analysis results. Preliminary findings indicate a promising correlation, suggesting graph centrality as a valuable tool in assembly analysis, enhancing efficiency and insight in critical component identification.

Non-carcinogenic risk assessment and sources apportionment of heavy metals in pomegranate in Anhui, China

This study examined the concentrations, sources, and health risks associated with 7 heavy metals in Chinese pomegranates. The results showed that the heavy metal in pomegranates was within standard limits, and the accumulation of heavy metals was low. PCA showed that Cd and Hg in pomegranates originate from irrigation water, Zn, As, and Cu from soil, and Cr and Pb from fertilizers. MLR and Monte Carlo sensitivity analysis showed that soil and irrigation water were the primary sources of heavy metals in pomegranates, with Zn, As, Cu, and Pb primarily originating from soil, and Cd, Cr, and Pb predominantly coming from irrigation water. The presence of organic matter and Ca in soil influenced heavy metal accumulation in pomegranates, other properties (Mg, Fe, and pH value in soil) have minor effects. The TTHQ for both adults and children remained below 1, while the TTRisk exceeded 1×10−4, mainly due to the hazardous impact of As in water. The upper 95% uncertainty limit indicated that residents were exposed to both non-carcinogenic and carcinogenic risks, primarily due to the toxicity of Cr in soil and As in water. Strengthening heavy metal screening in soil and water and enhancing pollution management policies are imperative.

A Monte Carlo Sensitivity Analysis for a Dimensionally Reduced-Order Model of the Aortic Dissection.

Aortic dissection is associated with a high mortality rate. Although computational approaches have shed light on many aspects of the disease, a sensitivity analysis is required to determine the significance of different factors. Because of its complex geometry and high computational expense, the three-dimensional (3D) fluid-structure interaction (FSI) simulation is not a suitable approach for sensitivity analysis. We performed a Monte Carlo simulation (MCS) to investigate the sensitivity of hemodynamic quantities to the lumped parameters of our zero-dimensional (0D) model with numerically calculated lumped parameters. We performed local and global analyses on the effect of the model parameters on important hemodynamic quantities. The MCS showed that a larger lumped resistance value for the false lumen and the tears result in a higher retrograde flow rate in the false lumen (the coefficient of variation, , the sensitivity , Spearman's coefficient, ). For the intraluminal pressure, our results show a significant role in the resistance and inertance of the true lumen (the coefficient of variation, , the sensitivity , and Spearman's coefficient, for the inertance of the true lumen). This study highlights the necessity of comparing the results of the local and global sensitivity analyses to understand the significance of multiple lumped parameters. Because of the efficiency of the method, our approach is potentially useful to investigate and analyze medical planning.

Distribution, Sources, and Health Risk of Polycyclic Aromatic Hydrocarbons in Farmland Soil of Helan, China

With the development of industry and agriculture, polycyclic aromatic hydrocarbons (PAHs) in the agricultural sector have gradually increased to different degrees, leading to an escalation in environmental pollution. In turn, this escalation has presented a significant possibility of endangering agricultural practices on farmland and has had a serious impact on regional sustainable development. Therefore, a total of 117 samples of soil were gathered to research the pollution level, distribution, sources, and health risk of PAHs in Helan farmland soils. A reference was used for the identification and quantification of PAH content using high-performance liquid chromatography (HPLC) with an ultraviolet detector, and their spatial distribution was analyzed utilizing the Arc Geographic Information System (ArcGIS). The source of PAHs was analyzed by absolute principal component scores/multiple linear regression (APCS-MLR). The lifetime cancer risk increment model and Monte Carlo sensitivity analysis were used to assess the potential health hazards to humans associated with PAHs in soil. Within the current study area, PAHs were higher in the northwest. The results showed that the total content of PAHs in Helan farmland soil ranged from 17.82 to 1544.73 ng·g−1 with a mean of 408.18 ng·g−1, which indicated the middle degree of pollution in farmland soil. The verification results of the APCS-MLR model showed that the correlation coefficient between the measured values and the predicted values ranged from 0.661 to 0.984, which suggested that the APCS-MLR model demonstrated favorable suitability for conducting source analysis of PAHs in the soil within the study region. Based on the contribution of PAHs from each source, the main sources of PAHs in Helan farmland soil were the combustion source (biomass, diesel, and natural gas combustion) and the transportation source (gasoline for vehicles and traffic exhaust emissions). The health risks’ estimation showed that PAHs in farmland soil did not have potential health risks for adults but represented a carcinogenic risk for children via the main exposure pathway of ingestion with the mean intake of 1.28 × 10−5. Meanwhile, the carcinogenic risks (CRs) of dermal contact for the mean value of adults (9.32 × 10−7) was found to be higher than that for children (3.18 × 10−8). From the Monte Carlo simulation, the soil particle uptake rate was the most sensitive to the health risks of children and adults with risk probabilities of 26% and 52%, and the risk probabilities from body weight were −11% and −1%, whose negative value indicated that the increase in body weight could reduce the health risks to human. These findings could provide reference for the study of soil organic pollution in Helan farmland soil and contribute significantly to the preservation of the ecological environment, maintaining human health and safety, and promoting the sustainable development of regional farmland.

Human health risk assessment of PM10-bound heavy metals and PAHs around the Latin America’s Largest opencast coal mine

Air pollution in opencast coal mine areas is a critical issue, resulting in harmful severe effects on human health. Therefore, it is essential to understand the air pollution factors and to assess the risks to humans. This study evaluated the potential risks (carcinogen and non-carcinogen) of inhalation exposure to PM10-bound heavy metals and Polycyclic Aromatic Hydrocarbons (PAHs) in an open pit mine in northern Colombia. During February-May 2022, PM10 samples were collected at eight sites. Heavy metals (i.e., Al, Cr(VI), Mn, Cu, Zn, As, Pb) and PAHs (thirteen priority PAHs, and one non-priority PAH) levels linked to PM10 were analyzed by X-ray fluorescence and gas chromatography–mass spectrometry, respectively. PM10 concentrations were found to range between 4.70 and 59.90 µg m−3. Out of the three different zones of the study area (i.e., North Zone, South Zone, and Populated Zone), the North Zone recorded the highest daily average concentrations of Cr(VI) (104.16 ng m−3), Mn (28.39 ng m−3), Cu (33.75 ng m−3), Zn (57.99 ng m−3), As (44.92 ng m−3), and Pb (27.13 ng m−3). The fraction of the analyzed heavy metals at all monitoring sites was 82%–89% for Al, followed by Cr(VI) with 3%–6%. Cr(VI) was the major contributor to the carcinogenic risk values, while Cu, Cr(VI), and As were the main drivers for the non-carcinogenic risk. The average cancer risk range for heavy metals was 3.30 × 10–04 –5.47 × 10–04. On the other hand, the cancer risk for PAHs exposure was acceptable. The average incremental lifetime cancer risk (ILCR) values varied between 2.87 × 10–07 and 4.21 × 10–07. Benzo[a]pyrene contributed to 54%–56% of the total risk from inhalation of PM10-bound PAHs, while Indeno[1,2,3-cd]pyrene contributed to 16%–19%. Based on the Monte Carlo sensitivity analysis, exposure to Cr(VI) was the main factor affecting cancer risk in the North, South, and Populated Zones. A suitable risk assessment and management plan requires understanding PM10-bound heavy metals and PAHs concentration levels as well as their potential health risks, mainly in open-cast coal mine zones. Our study found that people living near open-pit mines face potential health risks, so it is crucial to establish policies and regulations to control emission sources.

Impact of Alcohol Consumption on Multiple Sclerosis Using Model-based Standardization and Misclassification Adjustment Via Probabilistic Bias Analysis.

The etiology of multiple sclerosis (MS) is still not well-demonstrated, and assessment of some risk factors like alcohol consumption has problems like confounding and measurement bias. To determine the causal effect of alcohol consumption on MS after adjusting for alcohol consumption misclassification bias and confounders. In a population-based incident case-control study, 547 patients with MS and 1057 healthy people were recruited. A minimally sufficient adjustment set of confounders was derived using the causal directed acyclic graph. The probabilistic bias analysis method (PBAM) using beta, logit-logistic, and triangular probability distributions for sensitivity/specificity to adjust for misclassification bias in self-reporting alcohol consumption and model-based standardization (MBS) to estimate the causal effect of alcohol consumption were used. Population attributable fraction (PAF) estimates with 95% Monte Carlo sensitivity analysis (MCSA) intervals were calculated using PBAM and MBS analysis. Bootstrap was used to deal with random errors. The adjusted risk ratio (95% MCSA interval) from the probabilistic bias analysis and MBS between alcohol consumption and MS using the three distribution was in the range of 1.93 (1.07 to 4.07) to 2.02 (1.15 to 4.69). The risk difference (RD) in all three scenarios was 0.0001 (0.0000 to 0.0005) and PAF was in the range of 0.15 (0.010 to 0.50) to 0.17 (0.001 to 0.47). After adjusting for measurement bias, confounding, and random error alcohol consumption had a positive causal effect on the incidence of MS.

Arsenic and fluoride exposure in drinking water caused human health risk in coastal groundwater aquifers

Groundwater (GW) is a precious resource for human beings as we depend on it as a source of fresh drinking water, agricultural practices, industrial and domestic uses, etc. Extreme exposure of arsenic (As) and fluoride (F-) concentrations along the coastal GW aquifers of “South 24 Parganas and East Medinipur” diluted the quality of GW and created serious health issues. Various chronic health disorders such as – black foot disease, fluorosis skin cancer, cardiac problems, and other water borne diseases have been noticed in these two coastal districts. The comprehensive entropy-weighted water quality index (EWQI) and health risk assessment (HRA) were applied to evaluate the quality of GW and probable health risks in the coastal districts. Monte Carlo simulation and sensitivity analysis methods were simultaneously adopted to identify the non-carcinogenic health risk assessment due to regular ingestion of contaminated GW. As the study region is densely populated and part of the Sundarbans Ramsar site, it has greater importance at the international level along with regional importance to address the GWQ of this region. The major findings of the present study highlight that almost 55% of the study area is confronting serious GW quality issues and associated probable health risk (HR) due to the intense accumulation of As and F- in the GW aquifers of the study area. Children's health is more vulnerable due to the consumption of As containing GW, and adults are highly affected due to the intake of F- bearing GW in the coastal districts. The findings of the current study will draw the attention of hydrologists, groundwater management authorities, government bodies, and NGOs to regulate and monitor the GW aquifers routinely, enhance GW quality, minimizing the health hazards and sustainable water management in a more scientific and sustainable way which must be advantageous for coastal people.

Cost-Effectiveness Analysis of Adjuvant Endocrine Therapy With Ovarian Suppression in Premenopausal Patients With Hormone Receptor-Positive Early Breast Cancer in China

PurposeEndocrine therapy combined with ovarian function suppression (OFS) is recommended in intermediate- or high-risk patients among premenopausal women with hormone receptor-positive early breast cancer. However, in China, the cost-effectiveness of this strategy compared with endocrine therapy alone is unclear. This study aimed to evaluate the long-term cost-effectiveness of tamoxifen (TAM), TAM+OFS, and exemestane plus OFS (EXE+OFS). MethodsOn the basis of prognostic data from the Suppression of Ovarian Function Trial (SOFT), cost data from the Hospital Information System of the West China Hospital of Sichuan University, and health utility values from the published literature, a Markov model was established. The incremental cost-effectiveness ratio (ICER) was used to compare the treatment strategies. ResultsIn a 25-year simulation of adjuvant therapy in Chinese women with early breast cancer, the total costs of TAM, TAM+OFS, and EXE+OFS were $7821, $9318, and $9445, respectively. The quality-adjusted life-years (QALYs) were 11.615, 11.896, and 11.734 years, respectively. Compared with TAM, the ICERs of TAM+OFS and EXE+OFS were $5,327.4021/QALY and $13,647.0588/QALY, respectively. The ICERs of TAM+OFS and EXE+OFS were below the threshold of $32,517/QALY. The reliability and stability of the simulation results were verified using Monte Carlo simulation and sensitivity analysis. ConclusionIn the context of limited resources in China, TAM+OFS and EXE+OFS are cost-effective options compared with TAM.

Chatter reliability of high speed cylindrical grinding with uncertain parameters

Cylindrical grinding is commonly used as the final process of machining, but its quality and efficiency are often limited by chatter phenomenon. Many research show that the result from conventional chatter analysis by using the deterministic parameters is not consistent with the practical working condition and the chatter reliability prediction in high speed cylindrical grinding with uncertain parameters is studied in this paper. The dynamic model of the cylindrical grinding system is established, and uncertain parameters such as mass, stiffness and damping coefficient are introduced, and the stability lobe diagrams with uncertain parameters were obtained. The reliability model of the grinding system was established, and the reliability was solved by using the Monte Carlo simulation method, the improved first-order second-moment method and the second-order second-moment method, respectively. Then, the chatter-free reliability of the system was predicted by using the reliability lobe diagram. The accuracy of the grinding reliability prediction results is verified by the experiment results. Furthermore, the Monte Carlo sensitivity analysis method is used to study the influence of random parameters on the grinding system, and the results show that the variation of modal mass has the greatest influence on the reliability of the grinding system.

Chemical and radiological human health risk assessment from uranium and fluoride concentrations in tap water samples collected from Shiraz, Iran; Monte-Carlo simulation and sensitivity analysis

ABSTRACT Uranium is one of the most critical components in the nuclear field due to its chemical and radiological properties. Since few studies were conducted on uranium concentration in drinking water in Iranian cities, this study aimed to determine the concentration of uranium and flouridein the samples of drinking water collected from tap water in Shiraz, Fars province. Fifty samples were examined for uranium and fluoride presence using the ICP-MS and spectrophotometry methods. Hazard Quotient (HQ) and sensitivity analysis (SA) using the Monte Carlo Simulation technique with 10,000 iterations were applied to determine the human carcinogenic and non-carcinogenic effects of Uranium and Fluoride in three different groups (children, teenagers, and adults). The results showed that uranium and fluoride concentrations varied between 0.18–1.12 μg/L and 0.06–0.56 mg/L, respectively, which is less than the EPA recommendation for both elements. pH, EC, and TDS values were in accordance with the standard, and a positive correlation was observed between fluoride and total dissolved solids (TDS). The highest risk of carcinogenicity was found in the adult group with a value of 3.24E-9, lower than the allowable limit (1E-6). The highest hazard quotient (HQ) for uranium and fluoride was obtained in children with 0.13 and 0.66. Monte Carlo simulation results showed that the hazard quotient (HQ) for both elements in all groups was less than one and consistent with the values of the deterministic method. Also, water swallowing rate and contaminant concentration were the most influential parameters in the sensitivity analysis.

A Review of Conventional versus Additive Manufacturing for Metals: Life-Cycle Environmental and Economic Analysis

Additive manufacturing (AM) is a recent emerging technology that is being adopted in various industry sectors and supply chains. Life cycle assessment (LCA) and life cycle costing (LCC) are powerful methods that can be used for assessing the environmental and economic performance of emerging manufacturing technologies. This study aims to evaluate the life cycle environmental impacts and cost of computerized numerical control-based (CNC) manufacturing and direct metal laser sintering technology (DMLS) through a cradle-to-gate life cycle analysis. This research has four main novel elements: (i) conducting a recent comprehensive review of metal AM and conventional manufacturing (CM) processes using a systematic method and meta-analysis (ii) comparing the conventional process “CNC machining” and the additive technology “direct metal laser sintering” from the environmental (LCA) and economic (LCC) perspectives, (iii) investigating the influence of geometry complexity and shape size factors on the environmental and cost performance of both manufacturing methods, and (iv) conducting a Monte Carlo simulation-based sensitivity analysis to tackle uncertainty in LCC input parameters. The midpoints and endpoints impact for CNC and AM processes were estimated using the Ecoinvent v3.8 database and ReCiPe (E) impact assessment method. The review revealed that global warming potential is one of the most widely studied environmental indicators; however, only 6% of the studies have investigated the life cycle economic impacts of AM technologies using sensitivity and uncertainty analysis. The results have shown that in terms of ReCiPe endpoints, DMLS has the highest environmental impact on human health while CM has more impact on the eco-system quality. Electricity consumption is the main contributor to environmental impact categories in both manufacturing technologies. This is due to the high electricity utilization for casting and milling conventionally manufactured parts and printing the AM parts. LCC net present values revealed that manufacturing all parts with AM costs 91% more compared to CNC. The LCC analysis has shown that AM is more suitable and cost-effective for parts with highly complex geometries. Whereas CNC machining was found to be economically feasible for large-sized and low-complexity parts. The Monte Carlo sensitivity analysis demonstrated that for the case of AM, the most significant parameter is the processing cost followed by material cost, which highlighted the importance of energy-efficient AM and dematerialization through design for circularity.

Monte Carlo simulation and sensitivity analysis of the Michaëlis-Menten kinetic equation for the CO2 inhibition response to O2 consumption during storage of fresh produce

Monte Carlo simulation and sensitivity analysis of the Michaëlis-Menten kinetic equation for the CO2 inhibition response to O2 consumption during storage of fresh produce

Comprehensive assessment on groundwater quality, pollution characteristics, and ecological health risks under seasonal thaws: Spatial insights with Monte Carlo simulations

Groundwater pollution is a major environmental concern and may persist under diverse ecological conditions. This study describes the seasonal impacts on pollution characteristics coupled with the degree of human health risks in groundwater. For this purpose, a highly populated city of Pakistan, Lahore was planned and about 50 groundwater samples were collected during the summer and winter seasons in 2021 and 2022. Various physiochemical parameters (pH, EC, TDS, and hardness), cations, anions, and toxic metals (As, Cd, Co, Cr, Mn, Ni, and Pb) were analyzed to evaluate the seasonal impacts on the groundwater quality. Besides, a Monte Carlo simulation model and sensitivity analysis were performed to obtain a high-level accuracy in cancer risk estimation. Results revealed that physiochemical parameters, cations, and anions in most of the samples were within the WHO limit. However, the heavy metals concentration was higher in winter than in summer season. Most metals showed a significant spatial variability were found in older settlements and densely populated areas of the city for both seasons. Factor analysis (FA) and cluster analysis (CA) revealed a significant anthropogenic contribution to Ni, Co, Cd, Cu, Cr, and Pb contaminations in groundwater. Drinking water quality indices (DWQI) and heavy metal pollution indices (HMEI) points in both seasons revealed a poor to moderated pollution pattern. Pollution load index (PLI) and potential ecological risk index (PERI) indicated the medium pollution with low risks. Through Monte Carlo simulation at 95% confidence level revealed that carcinogenic risks associated with As, Cr, Cd, and Ni has exceeded from safer limits. Consequently, winter season was perceived more sensitive owing to the substantial increase of pollutants proclamation in groundwater and posturing a serious threat to humans. Hence, instant remedial actions are suggested to ensure safe drinking water.