Monte Carlo Risk Analysis Research Articles

Product Selection Toward High-Value Hydrogen and Bamboo-Shaped Carbon Nanotubes from Plastic Waste by Catalytic Microwave Processing.

The escalating levels of plastic waste and energy crises underscore the urgent need for effective waste-to-energy strategies. This study focused on converting polypropylene wastes into high-value products employing various iron-based catalysts and microwave radiative thermal processing. The Al-Fe catalysts exhibited exceptional performance, achieving a hydrogen utilization efficiency of 97.65% and a yield of 44.07 mmol/g PP. The gas yields increased from 19.99 to 94.21 wt % compared to noncatalytic experiments. Furthermore, this catalytic system produced high-value bamboo-shaped carbon nanotubes that were absent in other catalysts. The mechanism analysis on catalytic properties and product yields highlighted the significance of oxygen vacancies in selecting high-value products through two adsorption pathways. Moreover, the investigation examined the variations in product distribution mechanisms between conventional and microwave pyrolysis, in which microwave conditions resulted in 4 times higher hydrogen yields. The technoeconomic assessment and Monte Carlo risk analysis further compared the disparity. The microwave technique had a remarkable internal rate of return (IRR) of 39%, leading to an income of $577/t of plastic with a short payback period of 2.5 years. This research offered sustainable solutions for the plastic crisis, validating the potential applicability of commercializing the research outcomes in real-world scenarios.

Smallholder cocoa agroforestry systems; is increased yield worth the labour and capital inputs?

CONTEXTSmallholder cocoa agroforestry systems provide most of the cocoa produced world-wide and can improve the livelihoods of rural families in the tropics. However, many smallholder cocoa producers experience low cocoa yields due to generally poor management practices, pests, disease, and low soil fertility. In Papua New Guinea, pest and disease management systems have been developed to provide improved yields and revenues, but there has been limited uptake. OBJECTIVEWe hypothesized that the low uptake of these systems may be due to the improved yields not being sufficient to justify the required increase in labour and capital inputs. Our objective was to assess if the integrated pest and disease management systems were financially viable. METHODSOur methods involved developing discounted cash flow models that incorporated Monte Carlo risk analysis software. We populated the models with labour inputs, costs, and revenue data collected in country, and from scientific literature. In addition to net present value, we evaluated the production systems with additional criteria we deemed appropriate for assessing the required labour inputs and capital outlays; return on labour; mean labour hours/day; mean annual capital outlays, mean hourly income, and mean annual income. RESULTS AND CONCLUSIONSOur results show that under the current cocoa prices being received, all the production systems had a negative net present value, and a mean hourly income that was below the Papua New Guinea minimum wage and less than 1 USD/h. Furthermore, the progressive addition of labour inputs and capital outlays of the pest and disease management systems resulted in mean hourly incomes that are less than currently being received. These decreased hourly earnings may explain the low adoption of these technologies. SIGNIFICANCEFinancial analyses of new smallholder agroforestry technologies should be used to evaluate the viability and likelihood of smallholder technology adoption. Previous research on cocoa pest and disease management systems failed to account for costs related to labour and capital inputs. Even if farm family labour does not incur a direct cash outflow, it does represent an opportunity cost to farmers. Including additional criteria that measure financial returns to farmers relative to labour and capital requirements should be a part these assessments. These additional criteria can aid in evaluating all the economic thresholds that may impact if and how smallholders will incorporate new agroforestry technologies, which can help improve the success for agriculture-based development projects throughout the global tropics.

Evaluating the sensitivity of SARS-CoV-2 infection rates on college campuses to wastewater surveillance.

As college campuses reopened in fall 2020, we saw a large-scale experiment unfold on the efficacy of various strategies to contain the SARS-CoV-2 virus. Traditional individual surveillance testing via nasal swabs and/or saliva is among the measures that colleges are pursuing to reduce the spread of the virus on campus. Additionally, some colleges are testing wastewater on their campuses for signs of infection, which can provide an early warning signal for campuses to locate COVID-positive individuals. However, a representation of wastewater surveillance has not yet been incorporated into epidemiological models for college campuses, nor has the efficacy of wastewater screening been evaluated relative to traditional individual surveillance testing, within the structure of these models. Here, we implement a new model component for wastewater surveillance within an established epidemiological model for college campuses. We use a hypothetical residential university to evaluate the efficacy of wastewater surveillance for maintaining low infection rates. We find that wastewater sampling with a 1-day lag to initiate individual screening tests, plus completing the subsequent tests within a 4-day period can keep overall infections within 5% of the infection rates seen with traditional individual surveillance testing. Our results also indicate that wastewater surveillance can effectively reduce the number of false positive cases by identifying subpopulations for surveillance testing where infectious individuals are more likely to be found. Through a Monte Carlo risk analysis, we find that surveillance testing that relies solely on wastewater sampling can be fragile against scenarios with high viral reproductive numbers and high rates of infection of campus community members by outside sources. These results point to the practical importance of additional surveillance measures to limit the spread of the virus on campus and the necessity of a proactive response to the initial signs of outbreak.

A comparative techno-economic assessment of biochar production from different residue streams using conventional and microwave pyrolysis

A comparative techno-economic assessment and Monte Carlo risk analysis is performed on large scale (3 tonne/h) biochar production plants for conventional (CPS) and microwave (MWP) pyrolysis using six different residue streams. Both plants are viable with minimum selling prices between € 436/tonne and € 863/tonne for CPS, and between € 564/tonne and € 979/tonne for MWP. The CPS is therefore more viable than MWP as it is a simpler and more established technology. However, a 20% biochar price increase due to higher biochar quality makes the MWP technology more viable. Nevertheless, the discounted payback period remains higher than this of CPS due to the increased CAPEX. Biochar price is the most important determinant of a biochar production plant’s feasibility, motivating the need for economic and market research on biochar prices in function of biochar characteristics to reduce fluctuations in widely varying biochar prices.

Defining optimal soybean seeding rates and associated risk across North America

AbstractSoybean [Glycine max (L.) Merr.] seeding rate research across North America is typically conducted in small geo‐political regions where environmental effects on the seeding rate × yield relationship are minimized. Data from 211 individual field studies (∼21,000 data points, 2007–2017) were combined from across North America ranging in yield from 1,000– 7,500 kg ha−1. Cluster analysis was used to stratify each individual field study into similar environmental (soil × climate) clusters and into high (HYL), medium (MYL), and low (LYL) yield levels. Agronomically optimal seeding rates (AOSR) were calculated and Monte Carlo risk analysis was implemented. Within the two northern most clusters the AOSR was higher in the LYL followed by the MYL and then HYL. Within the farthest south cluster, a relatively small (±15,000 seeds ha−1) change in seeding rate from the MYL was required to reach the AOSR of the LYL and HYL, respectively. The increase in seeding rate to reach the LYL AOSR was relatively greater (5x) than the decrease to reach the HYL AOSR within the northern most cluster. Regardless, seeding rates below the AOSR presented substantial risk and potential yield loss, while seeding rates above provided slight risk reduction and yield increases. Specific to LYLs and MYLs, establishing and maintaining an adequate plant stand until harvest maximized yield regardless of the seeding rate, while maximizing seed number was important with lower seeding rates. These findings will help growers manage their soybean seed investment by adjusting seeding rates based upon the productivity of the environment.

Economic and Risk Analyses of an Industrial N,N‐Dimethylformamide Recovery Process

AbstractThe industrial N,N‐dimethylformamide (DMF) recovery plant is usually based on a two‐column sequence (TCS) and the capacity of the wastewater treatment process is larger than 2000 kg h−1. Here, a dividing‐wall column (DWC)‐based DMF recovery plant is proposed. The DWC avoids the remixing effect and reduces the utility duties by about 18.1 % and the CO2 emissions by 15.2 %, compared to the TCS. The DWC effectively decreases the total annual cost by 3.4 % compared to that of the TCS and achieves a profitability index of 2.19 and an internal rate of return of 40.45 %. By Monte Carlo risk analysis, the DWC can increase the chance of earning the profit by up to 16 % compared to the TCS.

Are portable sawmills a financially viable option for economic development in tropical forests?

Community forest enterprises facilitated by non-governmental organizations (NGOs), using portable sawmills to produce rough-sawn lumber is one approach being pursued by tropical forest indigenous communities to improve their livelihoods. To investigate the profitability of portable sawmills operated by community forest enterprises, we developed a discounted cash flow model with a Monte Carlo risk analysis simulation. We populated this model using forest inventory data from six forest sites in Papua New Guinea, combined with cost and revenue data collected in country. We found that the application of this small-scale native forest management model has a high likelihood of producing a negative net present value (NPV). The cash outflows to produce the lumber are found to be consistently greater than the cash inflows from lumber sales, resulting in a probability of achieving a positive NPV of 0.04. If only the most valuable species are harvested the probability of achieving a positive NPV increased to 0.56. However, the communities would be at risk of overexploiting or high-grading their forests. We recommend that future community forestry projects utilizing portable sawmills explore value adding opportunities for rough-sawn lumber to overcome the high costs of portable sawmill operations and reduce the potential occurrence of forest high-grading.

Methods for Predicting Behavior of Elephant Flows in Data Center Networks

Several Traffic Engineering (TE) techniques based on SDN (Software-defined networking) proposed to resolve flow competitions for network resources. However, there is no comprehensive study on the probability distribution of their throughput. Moreover, there is no study on predicting the future of elephant flows. To address these issues, we propose a new stochastic performance evaluation model to estimate the loss rate of two state-of-art flow scheduling algorithms including Equalcost multi-path routing (ECMP), Hedera besides a flow congestion control algorithm which is Data Center TCP (DCTCP). Although these algorithms have theoretical and practical benefits, their effectiveness has not been statistically investigated and analyzed in conserving the elephant flows. Therefore, we conducted extensive experiments on the fat-tree data center network to examine the efficiency of the algorithms under different network circumstances based on Monte Carlo risk analysis. The results show that Hedera is still risky to be used to handle the elephant flows due to its unstable throughput achieved under stochastic network congestion. On the other hand, DCTCP found suffering under high load scenarios. These outcomes might apply to all data center applications, in particular, the applications that demand high stability and productivity.

Optimal integrated sizing and operation of a CHP system with Monte Carlo risk analysis for long-term uncertainty in energy demands

In this study a probabilistic approach for optimal sizing of cogeneration systems under long-term uncertainty in energy demand is proposed. A dynamic simulation framework for detailed modeling of the energy system is defined, consisting in both traditional and optimal operational strategies evaluation. A two-stage stochastic optimization algorithm is developed, adopting Monte Carlo method for the definition of a multi-objective optimization problem. An Italian hospital facility has been used as a case study and a gas internal combustion engine is considered for the cogeneration unit. The results reveal that the influence of uncertainties on both optimal size and annual total cost is significant. Optimal size obtained with the traditional deterministic approach are found to be sub-optimal (up to 30% larger) and the predicted annual cost saving is always lower when accounting for uncertainties. Pareto frontiers of different CHP configurations are presented and show the effectiveness of the proposed method as a useful tool for risk management and focused decision-making, as tradeoffs between system efficiency and system robustness.

Health risk assessment of inhalation exposure of irrigation workers and the public to trihalomethanes from reclaimed water in landscape irrigation in Tianjin, North China

To estimate the concentration in air and the cancer risk of irrigation workers and the public exposed to the total trihalomethanes (TTHMs) in reclaimed water used for landscape irrigation, a probabilistic health risk assessment was conducted through the integrated use of one-dimensional (1-D) and two-dimensional (2-D) Monte Carlo simulations. Before the 2-D simulation, a sensitivity analysis corresponding to the 1-D simulation was carried out to identity the factors most affecting the outputs. The results reveal that the TTHM concentration level and cancer risk for workers’ exposure is much higher than that for public exposure in landscape irrigation. Moreover, the most influential factors are quite different for workers’ exposure and public exposure. The 2-D Monte Carlo risk analysis result for the workers indicated that the lowest-risk, highest-risk and two critical points for irrigation height are 0.7m, 1.53m, 1.4m and 1.65m when the mean value of the risk is selected as the reference statistic for risk management. Based on the risk assessment results, different measures can be suggested for the risk control of different populations. Furthermore, the influential variables should be better characterized to improve the accuracy of health risk assessment

PRESENCIA DE Trypanosoma sp. EN SAJINOS (Tayassu tajacu) CRIADOS EN CAUTIVERIO EN EL TRÓPICO PERUANO

The objective of the present study was to determine the presence of Trypanosoma sp. in collared peccaries reared in captivity in Iquitos and Moyobamba area. Blood samples were collected from 40 animals from 3 months to 2 years of age. Samples were processed by the microcapillary or Woo technique and the blood smear. None of the samples resulted positive to Trypanosoma sp., and probably the low sensitivity of the techniques affected the results. The Monte Carlo risk analysis (@Risk) indicated that the probability to obtain an infected animal in the Moyobamba and Iquitos area was 0.02% and that 95% of the samples were within an interval of 0.006 a 0.854%.

An Offshore Risk Analysis Method Using Fuzzy Bayesian Network

The operation of an offshore installation is associated with a high level of uncertainty because it usually operates in a dynamic environment in which technical and human and organizational malfunctions may cause possible accidents. This paper proposes a fuzzy Bayesian network (FBN) approach to model causal relationships among risk factors, which may cause possible accidents in offshore operations. The FBN model explicitly represents cause-and-effect assumptions between offshore engineering system variables that may be obscured under other modeling approaches like fuzzy reasoning and Monte Carlo risk analysis. The flexibility of the method allows for multiple forms of information to be used to quantify model relationships, including formally assessed expert opinions when quantitative data are lacking in early design stages with a high level of innovation or when only qualitative or vague statements can be made. The model is also a modular representation of uncertain knowledge due to randomness and vagueness. This makes the risk and safety analysis of offshore engineering systems more functional and easier in many assessment contexts. A case study of the collision risk between a floating production, storage and offloading unit and the authorized vessels due to human errors during operation is used to illustrate the application of the proposed model.

Political risk in light rail transit PPP projects

Since 2003 public–private partnerships (PPPs) have represented between 10 and 13.5% of the total investment in public services in the UK. The macro-economic and political benefits of PPPs were among the key drivers for central government’s decision to promote this form of procurement to improve UK public services. Political support for a PPP project is critical and is frequently cited as the most important critical success factor. This paper investigates the significance of political support and reviews the treatment of political risk in a business case by the public sector project sponsor for major UK-based light rail transit PPP projects during their development stage. The investigation demonstrates that in the early project stages it is not traditional quantitative Monte Carlo risk analysis that is important; rather it is the identification and representation of political support within a business case together with an understanding of how this information is then used to inform critical project decisions.

Biometric Fingerprinting for Visa Application: Device and Procedure Are Risk Factors for Infection Transmission

BackgroundBiometric fingerprint identity verification is currently introduced in visa application and entry screening at border control. The system implies physical contact between the skin and the surface of the fingerprint‐capturing and reading devices.AimTo assess the risk of infection transmission through fingerprinting.MethodsThe medical literature was reviewed for the potential of microorganisms to be carried on the skin of hands in the community, to be transferred from hands to inanimate surfaces, to survive on surfaces, and to be transferred in doses exceeding the infectious dose. The fingerprinting procedures as currently applied were reviewed.ResultsFactors that favor transfer of microorganisms are large skin–surface contact between flat fingers (2 × 20 cm2) and fingerprint‐capturing device, nonporous contact surface, large overlap of contact surface and short turnaround time between successive applicants, high contact pressure, and difficulties to disinfect devices. Transmission risk exists for enteric viruses (rotavirus, norovirus, and hepatitis A virus), respiratory viruses (respiratory syncytial virus, rhinovirus, influenzavirus, etc.), and enteropathogenic bacteria with low infectious doses (Shigella dysenteriae, Enterohemorrhagic Escherichia coli, etc.). Using Monte Carlo risk analysis on US data, transmission of human rotavirus is estimated at 191 [95% credible intervals (CI) 0–289] per million fingerprint‐capturing procedures. Application of 70% isopropyl hand rub and 85% ethanol hand gel reduces the risk to 77 (95% CI 0–118) and 0.3 (95% CI 0–0.3) transmissions per million procedures, respectively.ConclusionsThe fingerprinting procedure as currently used is associated with a risk of infection transmission. Simple hygienic measures can considerably reduce this transmission risk.

Economic evaluation of the solar carbothermic reduction of ZnO by using a single sensitivity analysis and a Monte-Carlo risk analysis

Abstract The technical feasibility of the solar carbothermal reduction of ZnO has been successfully demonstrated in a pilot plant. The economics of this process is addressed by means of a single sensitivity analysis and a Monte-Carlo risk analysis. A medium-term and a long-term scenario have been investigated, each for a 5 and a 30 MWth plant. For a discount rate of 15% the zinc production costs vary between 482 and 245 $/t for the medium-term scenario and between 312 and 146 $/t for the long-term scenario, respectively. These costs do not account for the zinc oxide input material. In addition, a risk analysis was conducted for the 30 MWth long-term scenario. For each input parameter, a probability distribution was estimated and the probability distribution of the zinc production cost was calculated by means of a Monte-Carlo method. The expected mean zinc production costs vary from 95 $/t for a discount rate of 0%–286 $/t for a discount rate of 40%.

Adaptive importance sampling for risk analysis of complex infrastructure systems

Complex civil infrastructure systems are typically exposed to random loadings and have a large number of possible failure modes, which often exhibit spatially and temporally variable consequences. Monte Carlo (level III) reliability methods are attractive because of their flexibility and robustness, yet computational expense may be prohibitive, in which case variance reduction methods are required. In the importance sampling methodology presented here, the joint probability distribution of the loading variables is sampled according to the contribution that a given region in the joint space makes to risk, rather than according to probability of failure, which is the conventional importance sampling criterion in structural reliability analysis. Results from simulations are used to intermittently update the importance sampling density function based on the evaluations of the (initially unknown) risk function. The methodology is demonstrated on a propped cantilever beam system and then on a real coastal dike infrastructure system in the UK. The case study demonstrates that risk can be a complex function of loadings, the resistance and interactions of system components and the spatially variable damage associated with different modes of system failure. The methodology is applicable in general to Monte Carlo risk analysis of systems, but it is likely to be most beneficial where consequences of failure are a nonlinear function of load and where system simulation requires significant computational resources.

Sensitivity analysis, Monte Carlo risk analysis, and Bayesian uncertainty assessment.

Standard statistical methods understate the uncertainty one should attach to effect estimates obtained from observational data. Among the methods used to address this problem are sensitivity analysis, Monte Carlo risk analysis (MCRA), and Bayesian uncertainty assessment. Estimates from MCRAs have been presented as if they were valid frequentist or Bayesian results, but examples show that they need not be either in actual applications. It is concluded that both sensitivity analyses and MCRA should begin with the same type of prior specification effort as Bayesian analysis.