Managing energy transition alongside environmental protection by making use of AI-led butanol powered SI engine optimization in compliance with SDGs

Enormous consumption of fossil fuel resources has risked energy accessibility in the upcoming years. The price fluctuation and depletion rate of fossil fuels instigate the urgent need for searching their reliable substitute. The current study tries to address these issues by presenting butanol as a replacement for gasoline in SI engines at various speeds and loading conditions. The emission and performance parameters were ascertained for eight distinct butanol-gasoline fuel blends. The oxygenated butanol substantially increases engine efficiency and boosts power with lower fuel consumption. The carbon emissions were also observed to be lower in comparison with gasoline. Furthermore, the Artificial Intelligence (AI) approach was used in predicting engine performance running on the butanol blends. The correlation coefficients for the data training, validation, and testing were found to be 0.99986, 0.99942, and 0.99872, respectively. It was confirmed that the ANN predicted results were in accordance with the established statistical criteria. ANN was paired with Response Surface Methodology (RSM) technique to comprehend the influence of the sole design parameters along with their statistical interactions controlling the responses. Similarly, the R2 value of responses in case of RSM were close to unity and mean relative errors (MRE) were confined under specified range. A comparative study between ANN and RSM models unveiled that the ANN model should be preferred. Therefore, a joint utilization of the RSM and ANN can be more effective for reliable statistical interactions and predictions.

Innovative technologies for removal of micro plastic: A review of recent advances

Plastics are becoming a pervasive pollutant in every environmental matrix, particularly in the aquatic environment. Due to increased plastic usage and its impact on human and aquatic life, microplastic (MP) pollution has been studied extensively as a global issue. The production of MP has been linked to both consumer and commercial practices. There is a significant amount of MP's that must be removed by wastewater treatment plants before they can be bioaccumulated. Many researchers have recently become interested in the possibility of eliminating MPs in wastewater treatment plants (WWTP). Many studies have analyzed MP's environmental effects, including its emission sources, distribution, and impact on the surrounding environment. The effectiveness of their removal by various wastewater treatment technologies requires a critical review that accounts for all these methods. In this review, we have covered the most useful technologies for the removal of MP during WWTP. The findings of this review should help scientists and policymakers move forward with studies, prototypes, and proposals for significant remediation impact on water quality.

Mo-based MXenes: Synthesis, properties, and applications

Ti-MXene allows a range of possibilities to tune their compositional stoichiometry due to their electronic and electrochemical properties. Other than conventionally explored Ti-MXene, there have been ample opportunities for the non-Ti-based MXenes, especially the emerging Mo-based MXenes. Mo-MXenes are established to be remarkable with optoelectronic and electrochemical properties, tuned energy, catalysis, and sensing applications. In this timely review, we systematically discuss the various organized synthesis procedures, associated experimental tunning parameters, physiochemical properties, structural evaluation, stability challenges, key findings, and a wide range of applications of emerging Mo-MXene over Ti-MXenes. We also critically examined the precise control of Mo-MXenes to cater to advanced applications by comprehensively evaluating the summary of recent studies using artificial intelligence and machine learning tools. The critical future perspectives, significant challenges, and possible outlooks for successfully developing and using Mo-MXenes for various practical applications are highlighted.

Experiments and CFD simulation of an air-conditioned tractor cabin for thermal comfort of tractor operators in Pakistan

Tractors are manufactured without air-conditioned cabins in Pakistan. This leads to thermal discomfort for tractor operators working under direct solar exposure. Therefore, this study aimed to design and install an air-conditioned cabin on a tractor. Experiments were undertaken to evaluate the installed cabin performance under two scenarios i.e., conventional (S-I) and enhanced (S-II) air distribution. Computational fluid dynamics (CFD) simulations were used to analyze airflow and calculate thermal comfort indices. The results showed that the air-conditioned cabin attained optimum thermal conditions under the enhanced air distribution scenario (S-II). In this scenario, the inside cabin temperature was an average of 27.4 °C, compared with 30.4 °C in S-I. The relative humidity remained similar in both scenarios, around 53 %. The temperature difference between the cabin and the ambient environment was 11.09 °C in S-II, aligning with the thermal comfort conditions outlined in ISO 14269-2. Furthermore, the CFD simulations showed a predicted mean vote (PMV) index of 0.61 and the percentage people dissatisfied (PPD) index of 26.5 %. These results also confirm the provision of optimum thermal conditions for operator inside the cabin. The simulations also demonstrated good agreement with experimental data, with a small difference in air temperature (2 °C) and relative humidity (5.8 %). In the light of these findings, this study recommends installation of air-conditioned cabin on tractors with enhanced air distribution (S-II) in Pakistan to improve thermal comfort of operators.

A Hybrid Data-Driven Metaheuristic Framework to Optimize Strain of Lattice Structures Proceeded by Additive Manufacturing

This research is centered on optimizing the mechanical properties of additively manufactured (AM) lattice structures via strain optimization by controlling different design and process parameters such as stress, unit cell size, total height, width, and relative density. In this regard, numerous topologies, including sea urchin (open cell) structure, honeycomb, and Kelvin structures simple, round, and crossbar (2 × 2), were considered that were fabricated using different materials such as plastics (PLA, PA12), metal (316L stainless steel), and polymer (thiol-ene) via numerous AM technologies, including stereolithography (SLA), multijet fusion (MJF), fused deposition modeling (FDM), direct metal laser sintering (DMLS), and selective laser melting (SLM). The developed deep-learning-driven genetic metaheuristic algorithm was able to achieve a particular strain value for a considered topology of the lattice structure by controlling the considered input parameters. For instance, in order to achieve a strain value of 2.8 × 10-6 mm/mm for the sea urchin structure, the developed model suggests the optimal stress (11.9 MPa), unit cell size (11.4 mm), total height (42.5 mm), breadth (8.7 mm), width (17.29 mm), and relative density (6.67%). Similarly, these parameters were controlled to optimize the strain for other investigated lattice structures. This framework can be helpful in designing various AM lattice structures of desired mechanical qualities.

Evaluating the Stress-Strain Relationship of the Additively Manufactured Lattice Structures

Extensive amount of research on additively manufactured (AM) lattice structures has been made to develop a generalized model that can interpret how strongly operational variables affect mechanical properties. However, the currently used techniques such as physics models and multi-physics simulations provide a specific interpretation of those qualities, and are not general enough to assess the mechanical properties of AM lattice structures of different topologies produced on different materials via several fabrication methods. To tackle this problem, this study develops an optimal deep learning (DL) model based on more than 4000 data points, which has been optimized by analyzing three different hyper-parameters optimization schemes including gradient boost regression trees (GBRT), gaussian process (GP), and random forest (RF) with different data distribution schemes such as normal distribution, nth root transformation, and robust scaler. With the robust scaler and nth root transformation, the accuracy of the model increases from R² = 0.85 (for simple distribution) to R² = 0.94 and R² = 0.88, respectively. After feature engineering and data correlation, the stress, unit cell size, total height, width, and relative density are chosen to be the input parameters to model the strain. The optimal DL model is able to predict the strain of different topologies of lattices (such as circular, octagonal, Gyroid, truncated cube, Truncated cuboctahedron, Rhombic do-decahedron, and many others) with decent accuracy (R² = 0.936, MAE = 0.05, and MSE = 0.025). The parametric sensitivity analysis and explainable artificial intelligence (by using DeepSHAP library) based insights confirm that stress is the most sensitive input to the strain followed by the relative density from the modeling perspective of the AM lattices. The findings of this study would be helpful for the industry and the researchers to design AM lattice structures of different topologies for various engineering applications.

On the Critical Heat Flux Assessment of Micro- and Nanoscale Roughened Surfaces

The boiling crisis or critical heat flux (CHF) is a very critical constraint for any heat-flux-controlled boiling system. The existing methods (physical models and empirical correlations) offer a specific interpretation of the boiling phenomenon, as many of these correlations are considerably influenced by operational variables and surface morphologies. A generalized correlation is virtually unavailable. In this study, more physical mechanisms are incorporated to assess CHF of surfaces with micro- and nano-scale roughness subject to a wide range of operating conditions and working fluids. The CHF data is also correlated by using the Pearson, Kendal, and Spearman correlations to evaluate the association of various surface morphological features and thermophysical properties of the working fluid. Feature engineering is performed to better correlate the inputs with the desired output parameter. The random forest optimization (RF) is used to provide the optimal hyper-parameters to the proposed interpretable correlation and experimental data. Unlike the existing methods, the proposed method is able to incorporate more physical mechanisms and relevant parametric influences, thereby offering a more generalized and accurate prediction of CHF (R² = 0.971, mean squared error = 0.0541, and mean absolute error = 0.185).

Critical care usage after major gastrointestinal and liver surgery: a prospective, multicentre observational study

BACKGROUND

Patient selection for critical care admission must balance patient safety with optimal resource allocation. This study aimed to determine the relationship between critical care admission, and postoperative mortality after abdominal surgery.

METHODS

This prespecified secondary analysis of a multicentre, prospective, observational study included consecutive patients enrolled in the DISCOVER study from UK and Republic of Ireland undergoing major gastrointestinal and liver surgery between October and December 2014. The primary outcome was 30-day mortality. Multivariate logistic regression was used to explore associations between critical care admission (planned and unplanned) and mortality, and inter-centre variation in critical care admission after emergency laparotomy.

RESULTS

Of 4529 patients included, 37.8% (n=1713) underwent planned critical care admissions from theatre. Some 3.1% (n=86/2816) admitted to ward-level care subsequently underwent unplanned critical care admission. Overall 30-day mortality was 2.9% (n=133/4519), and the risk-adjusted association between 30-day mortality and critical care admission was higher in unplanned [odds ratio (OR): 8.65, 95% confidence interval (CI): 3.51-19.97) than planned admissions (OR: 2.32, 95% CI: 1.43-3.85). Some 26.7% of patients (n=1210/4529) underwent emergency laparotomies. After adjustment, 49.3% (95% CI: 46.8-51.9%, P<0.001) were predicted to have planned critical care admissions, with 7% (n=10/145) of centres outside the 95% CI.

CONCLUSIONS

After risk adjustment, no 30-day survival benefit was identified for either planned or unplanned postoperative admissions to critical care within this cohort. This likely represents appropriate admission of the highest-risk patients. Planned admissions in selected, intermediate-risk patients may present a strategy to mitigate the risk of unplanned admission. Substantial inter-centre variation exists in planned critical care admissions after emergency laparotomies.