Analyzing sustainability in bread production: a life cycle assessment approach to energy, exergy and environmental footprint.

Bread production is a pivotal component of global nutrition. However, its extensive production imposes significant strain on resources and energy, resulting in substantial environmental consequences. This study focuses on a multidimensional assessment of the environmental sustainability of the bread life cycle as a case study in Iran. By integrating four life cycle assessment (LCA) methods, this research demonstrates a comprehensive analysis of environmental effects, energy consumption, and exergy demand in bread production. It also identifies the hotspot stages and inputs within the bread production chain. Eventually, it proposes strategies for mitigating the environmental impacts in line with sustainable development goals. Data collection involved questionnaires by face-to-face interviews. The LCA evaluation was conducted using SimaPro software. Sustainability analysis was assessed using four different methods: CML, ReCiPe, cumulative energy demand (CED), and cumulative exergy demand (CExD) method, from cradle to bakery gate. The CML method results indicate that the highest environmental impacts are associated with marine aquatic ecotoxicity (157.04 to 193.36 kg 1,4-DB eq), fossil fuel depletion (11.05 to 12.73 MJ), eutrophication (4.20 × 10-3 to 4.70 × 10-3 kg PO4-3 eq), acidification (8.09 × 10-3 to 9.16 × 10-3 kg SO2 eq), and global warming (0.61 to 0.69 kg CO2 eq). The ReCiPe method highlights wheat production stages and gas consumption as the most significant contributors to damage in terms of human health, ecosystems, and resource consumption indicators. The CED method reveals that fossil energy accounts for over 97% of the energy consumed during the bread life cycle. Energy consumption per kilogram of bread ranges from 12.07 to 13.93 MJ. The CExD method for producing 1 kg of traditional bread falls between 32.25 and 35.88 MJ. More than 60% of this value is attributed to renewable resources of water used in irrigation during the wheat farming stage, while over 35% is linked to non-renewable fossil resources, primarily due to the consumption of natural gas in bakery operations. To assess the potential decrease in environmental emissions, a sensitivity analysis was performed, considering the effects of substituting natural gas with biogas and grid electricity with photovoltaic electricity in the bakery. Then, three improved scenarios were developed, each demonstrating effective reductions in environmental impacts, with the most remarkable decreases observed in marine aquatic ecotoxicity (55%) and fossil fuel depletion (44%). Overall, the findings demonstrate that Sangak bread production exhibits a more environmentally friendly profile than other types of bread. These results can guide decision-makers in the bread production industry towards implementing sustainable practices that prioritize resource efficiency and environmental conservation. Also, stakeholders can develop strategies to reduce the environmental impacts and work towards a more sustainable future.

Comparative Analysis of Soft Computing Models for Predicting Viscosity in Diesel Engine Lubricants: An Alternative Approach to Condition Monitoring.

The viability of employing soft computing models for predicting the viscosity of engine lubricants is assessed in this paper. The dataset comprises 555 reports on engine oil analysis, involving two oil types (15W40 and 20W50). The methodology involves the development and evaluation of six distinct models (SVM, ANFIS, GPR, MLR, MLP, and RBF) to predict viscosity based on oil analysis results, incorporating metallic and nonmetallic elements and engine working hours. The primary findings indicate that the radial basis function (RBF) model excels in accuracy, consistency, and generalizability compared with other models. Specifically, a root mean square error (RMSE) of 0.20 and an efficiency (EF) of 0.99 were achieved during training and a RMSE of 0.11 and an EF of 1 during testing, utilizing a 35-network topology and an 80/20 data split. The model demonstrated no significant differences between actual and predicted datasets for average and distribution indices (with P-values of 1.00). Additionally, robust generalizability was exhibited across various training sizes (ranging from 50 to 80%), attaining a RMSE between 0.09 and 0.20, a mean absolute percentage error between 0.23 and 0.43, and an EF of 0.99. This study provides valuable insights for optimizing and implementing machine learning models in predicting the viscosity of engine lubricants. Limitations include the dataset size, potentially affecting the generalizability of findings, and the omission of other factors impacting engine performance. Nevertheless, this study establishes groundwork for future research on the application of soft computing tools in engine oil analysis and condition monitoring.

Pattern recognition-based Raman spectroscopy for non-destructive detection of pomegranates during maturity.

In this study, the feasibility of utilizing Fourier transform Raman spectroscopy, combined with supervised and unsupervised pattern recognition methods was considered, to distinguish the maturity stage of pomegranate "Ashraf variety" during four distinct maturity stages between 88 and 143 days after full bloom. Principal component analysis (PCA) as an unsupervised pattern recognition method was performed to verify the possibility of clustering of the pomegranate samples into four groups. Two supervised pattern recognition techniques namely, partial least squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA) were also used as powerful supervised pattern recognition methods to classify the samples. The results showed that in all groups of samples, the Raman spectra of the samples were correctly clustered using PCA. The accuracy of the SIMCA classification for differentiation of four pomegranate groups was 82%. Also, the overall discriminant power of PLS-DA classes was about 96%, and 95% for calibration and validation sample sets, respectively. Due to the misclassification among different classes of immature pomegranates, that was lower than the expected, it was not possible to discriminate all the immature samples in individual classes. However, when considering only the two main categories of "immature" and "mature", a reasonable separation between the classes were obtained using supervised pattern recognition methods of SIMCA and PLS-DA. The SIMCA based on PCA modeling could correctly categorize the samples in two classes of immature and mature with classification accuracy of 100%.

Optimal pretreatment determination of kiwifruit drying via online monitoring.

BACKGROUND: Pre-treating is a crucial stage of drying process. The best pretreatment for hot air drying of kiwifruit was investigated using a computer vision system (CVS), for online monitoring of drying attributes including drying time, colour changes and shrinkage, as decision criteria and using clustering method. Slices were dried at 70 °C with hot water blanching (HWB), steam blanching (SB), infrared blanching (IR) and acid ascorbic 1% w/w (AA) as pretreatments each with three durations of 5, 10 and 15 min. RESULTS: The results showed that the cells in HWB-pretreated samples stretched without any cell wall rupture, while the highest damage was observed in AA-pretreated kiwifruit microstructure. Increasing duration of AA and HWB significantly lengthened the drying time while SB showed opposite results. The drying rate had a profound effect on the progression of the shrinkage. The total colour change of pretreated samples was higher than those with no pretreatment except for AA and HWB. The AA could well prevent colour change during the initial stage of drying. Among all pretreatments, SB and IR had the highest colour changes. CONCLUSION: HWB with a duration of 5 min is the optimum pretreatment method for kiwifruit drying. © 2016 Society of Chemical Industry.

Enhancement of biogas production by co-digestion of potato pulp with cow manure in a CSTR system.

Anaerobic digestion (AD) process is a well-established method to generate energy from the organic wastes both from the environmental and economical perspectives. The purpose of present study is to evaluate energy production from potato wastes by incorporating cow manure into the process. Firstly, a laboratory pilot of one-stage biogas production was designed and built according to continuously stirred tank reactor (CSTR) system. The setup was able to automatically control the environmental conditions of the process including temperature, duration, and rate of stirring. AD experiment was exclusively performed on co-digestion of potato peel (PP) and cow manure (CM) in three levels of mixing ratio including 20:80, 50:50, 80:20 (PP:CM), and 0:100 as control treatment based on the volatile solid (VS) weight without adding initial inoculums. After hydraulic retention time (HRT) of 50 days on average 193, 256, 348, and 149 norm liter (LN) (kg VS)(-1), methane was produced for different mixing ratios, respectively. Statistical analysis shows that these gas productions are significantly different. The average energy was determined based on the produced methane which was about 2.8 kWh (kg VS)(-1), implying a significant energy production potential. The average chemical oxygen demand (COD) removal of treatments was about 61%, showing that it can be leached significantly with high organic matter by the employed pilot. The energy efficiency of 92% of the process also showed the optimum control of the process by the pilot.

Evaluation the effects of some relevant parameters on elastic modulus of pumpkin seed and its kernel.

The elastic modulus of two varieties of Iranian pumpkin seed and its kernel (namely, Zaria and Gaboor) were evaluated as a function of size (large, medium, and small), loading rate (2, 5, 8, and 10 mm/min), and moisture content (4, 7.8, 14, and 20% d.b) under quasistatic compression loading. The results showed that elastic modulus of pumpkin seed and its kernel decreased with increasing moisture content and also increasing loading rate, for the varieties under study. The average modulus of elasticity of pumpkin seed from 68.86 to 46.65 Mpa and from 97.14 to 74.93 Mpa was obtained for moisture levels ranging from 4 to 20%, for Zaria and Gaboor varieties, respectively. The elastic modulus of pumpkin seed decreased from 73.55 to 43.04 Mpa and from 101.83 to 71.32 Mpa with increasing loading rate from 2 to 10 mm/min for Zaria and Gaboor varieties, respectively.