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Based on the high volume of research pertaining to household food waste (FW) generation, a significant proportion of existing studies present findings that lack comparability and general transferability, largely due to their confinement to individual countries or regions. Likewise, results frequently exhibit considerable variability and cross-study incongruities. Merging individual studies into broader scoping reviews can elucidate the underlying reasons for this variability between studies, enhance overall comparability and generalizability of findings, and subsequently expand overall applicability. The current scoping review and pooled analysis examined household FW generation data from high-income countries using the Population Concept Context (PCC) framework, identifying and analyzing empirical studies from 2010 to 2022. Overall, 56 studies from 24 countries were identified, resulting in a pooled mean FW volume of 42.86 kg/cap/yr. The Asia-Pacific region exhibited the highest mean FW volume (70.28 kg/cap/yr), while Europe had the lowest (34.45 kg/cap/yr). Within Europe, non-EU countries reported higher mean volumes (56.88 kg/cap/yr) than EU member states (34.33 kg/cap/yr). Methods involving self-measurement by householders recorded lower FW volumes compared to those measured by third parties (FW researchers). Fruits and vegetables were the most frequently discarded items, with a mean volume of 18.75 kg/cap/yr and 32.34% of total FW volumes. The presented review highlights the importance of considering study methodology, location, and FW composition when interpreting household FW data. It also serves a useful tool for designing household FW studies and improving the quality of data going forward. [ABSTRACT FROM AUTHOR]

A hybrid programming methodology was proposed to derive a simple empirical formulation for the estimation of the required pipe diameter in the sizing problems (Type 3) of pipe distribution systems. The model was derived based on multiple regression-based analysis using the Richardson's extrapolation approach and the Levenberg–Marquardt algorithm with double precision. The proposed formulation was developed using a total of 300,000 different data points within the framework of MATLAB and DataFit scientific software. The application of the model was explored for a wide range of five fundamental pipeline design variables [absolute roughness of the pipe wall (ε=0–9 mm), water temperature (T=5°C–100°C), pipe length (L=5–500 m), flow rate (Q=0.01–1 m3/s), and head loss (Δh=1–30 m)] and tested against a total of 10,000 additional computational scenarios and the available models reported in the literature. The uncertainty prediction of the proposed formula was quantified and compared with those of existing prediction models. For the new empirical equation, the mean prediction errors between the estimated and the theoretical diameter values (calculated from the numerical solution of the Colebrook–White equation) were significantly smaller than those of existing models. Moreover, the narrowest uncertainty bands, the lowest 95% confidence prediction error intervals, and the lowest amounts of expanded uncertainty (U95) were achieved for the proposed model. Other statistics (e.g., mean absolute relative error, absolute relative error, coefficient of variation of root mean squared error, determination coefficient) also corroborated that the proposed empirical model produced realistic estimations that were superior to those obtained from other well-known explicit models in the literature. The findings of this study concluded that the computational analysis yielded a simple mathematical structure to be easily and accurately used for educational and practical purposes. [ABSTRACT FROM AUTHOR]

• ANN outnumbered other standalone AI models (single models) applied to WWTPs. • Hybrid models were relatively more accurate than the standalone models. • Most of hybrid models were classified as CI-metaheuristic models. • FL was the most suitable model for the incomplete data sets. • Despite recent developments, industrial deployment is lacking. Increasing energy efficiency in wastewater treatment plants (WWTPs) is becoming more important. An emerging approach to addressing this issue is to exploit development in data science and modelling. Deployment of sensors to measure various parameters in WWTPs opens greater opportunities for exploiting the wealth of data. Artificial intelligence (AI) is emerging as a solution for automation and digitalization in the wastewater sector. This review aims to comprehensively investigate, summarize and analyze recent developments in AI methods applied to the modelling of WWTPs. The review shows that among the standalone models, Artificial Neural Networks (ANN) was the most popular model followed by, in descending order: Decision Trees (DT), Fuzzy Logic (FL), Genetic algorithm (GA) and Support Vector Machine (SVM). In the case of incomplete data, FL was the most frequently used method as it uses linguistic expert rules to find an approximation for the missing data. Regarding accuracy and precision, hybrid models demonstrated relatively better performance than the standalone ones. Among these models, the Machine Learning (ML)-metaheuristic, which integrates an AI model with a bioinspired optimization method, was the most preferred type as it was used in more than 45% of the hybrid models. Correlation coefficient (R), Correlation of Determination (R2) and Root Mean Square Error (RMSE) were the frequently used metrics for model performance evaluation. Finally, the review shows that despite recent developments, industrial deployment is still lacking. The industrial application requires close interaction of interested parties, among which research institutes, private sector and public sector play an inevitable role. The future research should focus on mitigating the barriers for more in-depth collaboration of interested parties and finding new paths for more cooperative and harmonized activity of them. [ABSTRACT FROM AUTHOR]

The discussers mentioned that "the developed explicit solution of Eq. (7) of the original paper is applicable only to the transitional region and cannot predict the pipe diameter for either the rough ( HT ht ) or the smooth ( HT ht ) flow regime because for these values, Eq. (7) yields HT ht ." These ranges correspond an absolute roughness of the pipe wall ( HT ht ) range of 0.000396-8.999052 mm. [Extracted from the article]

• Available literature on LCA and structural characteristics of buildings is reviewed. • Low-rise buildings have received more attention than high-rise buildings. • Energy use and greenhouse gas emissions are the main focus of the existing literature. • The use phase of buildings accounts for the majority of life cycle impacts. • The construction phase may play a large role, particularly in energy efficient buildings. An overview of current status of the available literature on Life Cycle Energy, Life Cycle Greenhouse gasses, and Conventional Life Cycle Assessment of commercial and residential buildings was presented with respect to their height. A narrative literature review was carried out to provide a comprehensive overview as well as to highlight the recent contributions related to the environmental evaluation of high-rise and low-rise buildings. The study was carried out by searching the databases of the Scopus and Elsevier in conjunction with ScienceDirect and Google Scholar databases. The reason for this was to cover the published papers in this field up to the highest degree of accuracy. By means of the search of publications quoting the use of LCA in construction sector for the period from 1997 to 2018, more than 230 peer-reviewed publications referencing the use of life cycle assessment in buildings have been identified. The review shows that low-rise buildings (1~5 floors) compared to high-rise ones (≥ 5 floors) received significant attention as the studies focusing on the life cycle assessment of low-rise buildings were about twice in number more than the studies related to the life cycle assessment of high-rise buildings. In case of high-rise buildings, commercial buildings gained more attention by over 60% of the reviewed studies, while for low-rise buildings, residential buildings took the leverage by accounting to over 70% of the reviewed studies. The more frequently studied life cycle stages were those related to the manufacturing and use phases. Similarly, the most considered impact categories were the global warming potential and embodied energy. The reported values for embodied energy of high-rise buildings had a great variation ranging from 0.533 MJ/m2 to 883.1 GJ/m2, while the same values for low-rise buildings ranged from 0.21 to 374.4 GJ/m2. In terms of global warming potential, high-rise buildings emitted 10 to 10,010 kg CO 2 -eq/m2 per year, however, some studies revealed the potential of timber structure in emission reduction by values ranging from 234.8 to 1338 kg CO 2 -eq/m2. The emissions associated by low-rise buildings ranged from 0.07 to 35,765 kg CO 2 -eq/m2, and the respective values for emission reduction by timber structures were between 12.9 and 361 kg CO 2 -eq/m2. The results also indicate that a wide range of building's lifespan varying from 20 to over 100 years were utilized in life cycle assessment of different types of buildings. Functional unit was also another parameter that showed a broad variation both in terms of unit and definition. While the majority of researchers considered "m2" as the functional unit (61%), "whole building" was also considered as the functional unit in almost 20% of the reviewed studies, indicating the lack of standardized definition for functional unit for more practical outcomes. Ecoinvent was the most referred inventory database (65%) for life cycle assessment of buildings followed by University of Bath ICE (11%), U.S. database (9%), and Australian material inventory database (7%). SimaPro dominated computer-aided softwares with 40% of citations among the reviewed studies. ATHENA Impact Estimator and GaBi software gathered the attention of the reviewed studies by 7.5% and 4%, respectively. The review finally highlights that the variations in building design (structure and materials), lifetime, functional unit, and scope restrict to compare the findings and results of studies with each other. [ABSTRACT FROM AUTHOR]

Artificial intelligence-based methodology [artificial neural network (ANN) and fuzzy logic] and a multiple regression-based analysis were conducted for modeling of the biogas production rate from a real full-scale sludge digestion process. In the computational analysis, five process-related parameters such as influent sludge flow rate, total solids content, total volatile solids content, alkalinity, and volatile fatty acids concentration were considered as the input variables for the proposed models. In the ANN-based modeling, a benchmark comparison of 11 backpropagation (BP) algorithms was employed in the first step, and the scaled conjugate gradient algorithm was chosen as the best BP algorithm in terms of their respective mean squared errors. According to the selected BP algorithm (scaled conjugate gradient BP), the number of neurons at the hidden layer was optimized as 14, and the coefficient of determination (R2) was obtained as 0.65 for the optimal three-layer ANN structure (5:14:1). In the second part of the study, a MISO (multiple-input single-output)-type fuzzy-logic model was developed, and five input variables were fuzzified in a knowledge-based manner. For the fuzzy subsets, trapezoidal membership functions were implemented with 10 and 20 levels, and a Mamdani-type fuzzy inference system (FIS) was used to employ 394 rules in the if–then form. The product, summation, and centroid methods were employed, respectively, for implication, aggregation, and defuzzification processes conducted in the FIS. Fuzzy logic-produced forecasts were compared with the estimations obtained from both ANN-based model and a polynomial multiple regression-based model (employed as the third part of this study) derived in this study. Findings of this study clearly indicated that compared to ANN model and conventional multiple regression approach, the proposed MISO fuzzy logic-based model produced smaller deviations and exhibited a superior predictive performance on forecasting biogas production rate from a full-scale treatment plant with a satisfactory R2 value of 0.88. [ABSTRACT FROM AUTHOR]

[Display omitted] • Electricity consumption is mapped using a combined cartographic methodology. • State of the sky impact is explored for the first time on energy management strategies. • LF strategy provides the best load monitoring by minimizing the generator uptime. • Energy management strategies play a pivotal role as hybrid solar system control tools. A novel optimized sizing and management strategy of a grid-connected hybrid photovoltaic (PV)-solar-battery-group system were proposed for the electrification of residential consumers in Northwest Africa (a case of Mauritania), and the influence of the state of the sky (clear, moderately overcast, and overcast) was analyzed according to the load flowing (LF) and the cycle charging (CC) strategies. In order to mitigate the pressure on the national grid, consolidate the consumer autonomy, minimize the cost of medium- and long-term consumption bills, and CO 2 -related emissions, a cartographic approach was conducted as the first attempt to map the electricity consumption potential for buildings in the city of Nouakchott (Mauritania) using a geo-referenced database. ArcGIS®, HOMER Pro®, and MATLAB® softwares were used for the establishment of the load profile, optimized sizing of the PV-batteries-group-grid system, and calculation of the lightness index, respectively. The LF strategy provided the best monitoring of the load throughout the day by minimizing the generator uptime. The techno-economic analysis revealed the values of cost of energy (COE) and net present cost (NPC) as follows: COE = $0.0549/kWh and NPC = $24,796 for the LF PV-batteries-grid strategy, COE = 0.0646 $/kWh and NPC = $23,380 for the CC PV-batteries-grid strategy, and COE = $0.17/kWh and NPC = $23,262 for the case of the grid on its own. [ABSTRACT FROM AUTHOR]

Based on the occurrence conditions of a thick and hard main roof and wedge-structure immediate roof in the Zhuxianzhuang Coal Mine, the fracture characteristics and instability migration law of a thick and hard roof (THR) were examined via physical simulations. Mining zones were divided with respect to the strata behaviors and roof control difficulty levels, and the principles and methods of zonal control under THR were put forward. This study proposed a coordinated control strategy of using confined blasting in water-filled deep holes, and reasonable support optimization, which could effectively reduce the roof fracture size, increases the supporting intensity and eliminate roof-control disasters. The length of confined blasting blocks and supporting intensity were calculated using a mechanical model for roof control in the strong strata behavior zone and less-strong strata behavior zone. These key parameters were determined as 20–25 m and 1.15–1.28 MPa, respectively, and the mining strategy was successfully applied in working face 880, performing high security and reasonable economical efficiency. [ABSTRACT FROM AUTHOR]

A prognostic approach based on a MISO (multiple inputs and single output) fuzzy logic model was introduced to estimate the pressure difference across a gas turbine (GT) filter house in a heavy-duty power generation system. For modelling and simulation of clogging of the GT filter house, nine real-time process variables (ambient temperature, humidity, ambient pressure, GT produced load, inlet guide vane position, airflow rate, wind speed, wind direction and PM10 dust concentration) were fuzzified using a graphical user interface within the framework of an artificial intelligence-based methodology. The results revealed that the proposed fuzzy logic model produced very small deviations and showed a superior predictive performance than the conventional multiple regression methodology, with a very high determination coefficient of 0.974. A complicated dynamic process, such as clogging phenomenonin heavy-duty GT system, was successfully modelled due to high capability of the fuzzy logic-based prognostic approach in capturing the nonlinear interactions. [ABSTRACT FROM AUTHOR]

The large consumption of natural gas, one of the most important energy sources in the world, necessitates reliable, precise, and accurate calculation of gas flow rate and amount in order to use this resource in an efficient and sustainable way. The present computational study investigates the possibilities of several soft-computing strategies in estimating the mass flow rate of natural gas flow stream (kg/h) (output variable) based on four input variables of orifice plate diameter ratio, differential pressure of orifice plate (kPa), operating pressure of the natural gas (bar), and operating temperature of the natural gas (°C). A genotype/phenotype genetic algorithm (gene expression programming (GEP) technique), two decision tree-based methods (random forest (RF), random tree (RT) models), and two kernel-based approaches (Gaussian process regression (GPR) and support vector machines (SVM) methods) were applied for the first time to predict gas mass flow rate. Coefficient of correlation (CC), mean absolute error (MAE), root mean square error (RMSE), Scattering index (SI), Nash–Sutcliffe efficiency (NSE), and mean absolute relative error (MARE) were computed as the statistical performance evaluators to determine of the best-performing soft-computing approach. The performance assessment indices corroborated the superiority of the Pearson VII universal kernel function-based GPR model (GPR-PUKF) model (CC = 0.9997, MAE = 64.8091 kg/h, RMSE = 248.7584 kg/h, SI = 0.0237, and NSE = 0.9993 for the testing dataset) over other data-intelligent models in predicting the gas mass flow rate. In addition, statistical results revealed that the predictions of the RF method were better than those of the GEP- and RT-based models, but the GEP approach showed the lowest performance among all applied models. Although the CC values of all models were satisfactory (>0.993), the percentile deviation of GPR model (1.7325%) from the actual values showed competitive lower values, indicating its superior performance than other models (GEP = 15.1436%, RF = 6.5403%, RT = 9.5576%, and SVM = 3.2107%). This study highlighted the significance of employing advanced soft-computing approaches in determining the mass flow rate of natural gas, a vital source of energy, as well as its value to the gas sector. [Display omitted] • Soft-computing implementation for prediction of natural gas mass flow rate. • Benchmarking of genetic/decision tree/kernel-based data-intelligent models. • Lower deviations (1.73% and 0.36%) of GPR-PUKF over other methods. • Superiority of RF (6.54%) over GEP (15.14%) and RT (9.56%) in terms of errors. • Flexibility of soft computation in highly nonlinear real-world gas measurement. [ABSTRACT FROM AUTHOR]

Owing to its importance in extraction of natural gas from underground gas storage as well as its crucial role in determination of final gas mixture in the production facilities of gas/oil industry, the dry content of wet gas mixture needs to be calculated precisely. The present study explores the potential of different soft-computing techniques in estimation of the dry gas flow rate (kg/h) (output variable) of wet gas mixture based on two input variables of wet gas flow rate (kg/h) and absolute gas humidity (g/m3). Decision tree-based methods (M5P tree, random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), kernel function-based approaches (Gaussian process regression (GPR) and support vector machines (SVM)), and non-parametric regression-based technique (multivariate adaptive regression splines (MARS)) were implemented for the first time to estimate the dry gas flow rate, and their respective prediction performances were analyzed statistically. Coefficient of correlation (CC), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE), Legates and McCabe's index (LMI), and Willmott's Index (WI) were used as the statistical indicators for validating the performance of each soft-computing model. While M5P model (MAE = 122.2382 kg/h, RMSE = 580.5626 kg/h, CC = 0.9875 for the testing data set) was better than other tree-based models (MAE = 363.2802–542.6119 kg/h, RMSE = 871.9363–1025.3444 kg/h, CC = 0.9587–0.9706 for the testing data set) and MARS model (MAE = 128.0083 kg/h, RMSE = 622.9515 kg/h, CC = 0.9852 for the testing data set), the statistical indicators approved the superiority of the radial basis kernel function-based GPR model (GPR-RBKF) model (MAE = 163.3266 kg/h, RMSE = 483.1359 kg/h, CC = 0.9915 for the testing data set) over other implemented models in predicting the dry gas flow rate. The findings highlighted the potential of soft-computing methodologies in precise estimation of dry gas flow rate in wet gas mixture, particularly, in situations where the measurement of such parameters with traditional deterministic models is practically not possible. [Display omitted] • Soft-computing methods for estimation of dry content of wet gas mixture. • Performance evaluation of M5P, RF, RT, REPT, GPR, SVM, and MARS. • Superiority of GPR-RBKF over other models in terms of accuracy. • Superiority of M5P over other tree decision tree models in terms of error. • Higher precision of Gaussian processing with kernel-based regression vector. [ABSTRACT FROM AUTHOR]

The present analysis was conducted as the first research to assess the techno-economic viability of the value-added by-products (struvite, blood meal, bone meal, and raw sheepskin) from a medium-scale sheep slaughterhouse facility with a slaughtering capacity of 300 sheep per day. For this aim, a comparative technical and economic feasibility analysis was performed to assess the synergistic use of slaughterhouse-oriented rendering wastes and struvite recovery from real sheep abattoir effluent within the framework of detailed cost breakdown, break-even point, and payback period analyses. The experimental findings clearly showed that under the optimal conditions (chemical combination of MgCl 2.6H 2 O + NaH 2 PO 4.2H 2 O, a molar ratio of Mg2+:NH 4 +-N:PO 4 3--P = 1.2:1:1, a reaction pH of 9.0, an initial ammonium concentration of 240 mg NH 4 +-N/L, and a reaction time of 15 min), struvite precipitation could effectively remove about 73%, 64%, 59%, and 82% of NH 4 +-N, TCOD, SCOD, and color, respectively, from the real sheep slaughterhouse waste stream. Based on various up-to-date techno-economic items considered within the break-even point analysis, the sheep slaughterhouse facility was estimated to achieve the targeted net income (€100/day) for any selling prices of €1041.30/ton, €640.05/ton, €263.72/ton, and €1.012/hide, respectively, for struvite, blood meal, bone meal, and raw sheepskin. Steel construction and chemicals were determined as the most costly components for CAPEX (capital expenditures) and OPEX (operating expenditures), respectively, and selling prices of bone meal and raw sheepskin were found to be the most critical income items on the profitability of the slaughterhouse facility. Co-monetary assessment of the struvite process and valorized compounds corroborated the economic viability of the proposed project with the payback periods of about 6.3 and 5.5 years, respectively, for the current market and the profit-oriented conditions without subsidy. The findings of this feasibility analysis, as the first of its own, could be used as guideline for simplifying the decision-making with regards to the feasibility of similar facilities and commercialization of profitable by-products. [Display omitted] • Income-generating real sheep slaughterhouse concept is described for the first time. • High profit corridor is reached when the sales price of struvite exceeds €1041.30/ton. • Daily net profit is predicted as €12.22/kg NH 4 +-N removed at optimum MAP conditions. • Facility can pay for itself less than 6 years for unsubsidized profit-oriented case. • Valorized by-products offer an eco-sustainable way for meat processing facilities. [ABSTRACT FROM AUTHOR]

Due to its importance in flow measurement and instrumentation, as well as its frequent application in differential pressure flowmeters, orifice discharge coefficient (C d) needs to be estimated precisely. In this study, different soft computing models (including multiple linear regression (MLR), group method of data handling (GMDH), multivariate adaptive regression splines (MARS), M5P tree model, and random forest (RF)) were employed for the first time in estimation of the C d value, and their respective prediction performances were analyzed statistically. Coefficient of correlation (CC), mean absolute error (MAE), root mean square error (RMSE), scattering index (SI), and Nash–Sutcliffe model efficiency coefficient (NSE) were used as the statistical indicators for validating the performance of each soft computing model. The statistical indicators approved the superiority of the RF model over the other models, while the MARS model also showed a competitive prediction potential over M5P, GMDH, and MLR models. The findings of this computational study clearly demonstrated that the implemented soft computing strategy had the capability to be used in precise estimation of the C d of the orifice meter, specifically, in situations where the measurement of the parameters in deterministic equation is not practically feasible. [Display omitted] • Differential pressure flowmeters were simulated using soft computing methodology. • MLR, GMDH, MARS, M5P, and RF were used to estimate C d for the first time. • Effectiveness of models was tested by CC, MAE, RMSE, SI, and NSE metrics. • RF outperformed other models, and MARS showed competitive prediction accuracy. • RF-based soft computation was of potential to be used in precise estimation of C d. [ABSTRACT FROM AUTHOR]

This study introduced an investigation to evaluate spatial and temporal variations of the wind potential for the techno-economic feasibility analysis of the energy production in Northwest Africa (a case of Mauritania). The present research was introduced as the first attempt to appraise the spatio-temporal influence of the wind energy production in Mauritania, and particularly focused on analyzing seasonal, daily, and turbulence index on the available wind potential in this region. Data measured every 10 min over one-year period were collected from eight sites (with three different height levels) located mainly on the west coast of Mauritania, and the annual average of the wind characteristics were determined. Power density, Weibull parameters, turbulence indices, and power-law exponents were estimated based on seasonal and daily wind analyses. Comparative studies of the power density potential of the wind on different sites were also conducted while investigating the influence of seasons, height of the wind turbines, wind directional distributions, and daily characteristics. Investigations regarding the generated energy from the wind turbine and the related capacity factor were performed based on eight particular wind turbines (Ecotècnia-44, Ecotècnia-48, Nordex-N50, Neg-Micon, Vestas-V66, Power-Wind-90, Bonus-2MW, and Vestas-V90). Results showed that the power-law exponent was higher where the turbulence index was low. The analysis of the power distribution allowed concluding on the energy availability according to the influent variables. Findings of the present techno-economic analysis (for electricity generation from the planned wind energy systems) revealed that the best cost of energy (ranging from 0.0187 €/kWh to 0.0596 €/kWh) was observed for the wind turbine Ecotècnia-48 on all sites. Image 1 • Spatio-temporal variations of the wind potential were explored in Mauritania for the first time. • Wind potential for electricity generation was characterized for eight sites with three height levels. • Wind direction was more stable in the rainy season than in dry season on all sites. • Annual mean turbulence index and power law exponent showed an opposite relationship. • Techno-economic feasibility analysis revealed that the best cost of energy was 0.0187 €/kWh. [ABSTRACT FROM AUTHOR]

Despite the remarkable potential for biogas production from livestock and organic wastes, the number of constructed biogas digesters for the continent is in the order of thousands and in case of Mauritania is lower than the number of the provinces. While majority of the existing digesters left far behind the expected efficiency, lack of a comprehensive economic assessment along with an efficient design questions their practical application. The present study introduced the first assessment to evaluate the biogas potential from livestock manures and waste from slaughterhouses in Northwest Africa (a case of Mauritania). Using ArcGIS® software, a database was conducted to build maps that show the amount of waste products, the potential of biogas, and equivalent amounts of energy. These were used to assess the potential of biogas and the corresponding potential energy for each geographical department in Mauritania. The results indicated that the southern provinces had the highest biogas potential with the maximum and the average values of 520 and 258.7 (±125.8) × 106 m3/year. On the other hand, the lowest biogas production potential (27.7 × 106 m3/year) was recorded for northern provinces with the maximum and the average values of 135 and 76.4 (±39.7) × 106 m3/year. The results showed that 63,579 × 106 kg of waste associated with livestock (cattle) and slaughterhouse applications were annually produced in the country. It was determined that this quantity could generate 2451 × 106 m3 of biogas per year, corresponding to an energy potential of 52,704 × 106 MJ/year. Considering the rapid depletion of conventional energy sources and the significance of biogas as a renewable fuel, a detailed feasibility analysis (for the biogas production in each province of Mauritania by means of community type fixed-dome digesters) was also performed in the scope of this study. The results of the comprehensive cost breakdown analysis revealed that the revenues obtained from sale of biogas-generated electricity and digested slurry (as fertilizer) could able to pay the initial investment within approximately 6.5 years without subsidy. The findings of this study, as the first of its own, could be used to comprehend how utilization of information such as slaughterhouse and livestock population, nutrition habitats, land-usage maps and geographic information system (GIS) can be employed to germinate a model for more comprehensive assessment of biogas production potential from livestock manure and slaughterhouse wastes, specifically in case of northern African countries. Moreover, in case of biogas plant, this model could be employed as a decision-making tool to identify the highly qualified location for construction of the biogas plant. • Mapping of biogas production potential is conducted in Mauritania for the first time. • 63,579 × 106 kg of waste can generate 2451 × 106 m3 of biogas per year in the country. • The southern provinces are found to have approximately 4 times more biogas potential. • Energy production in Mauritania heavily depends on oil, natural gas, and wood fuels. • A nationwide biogas digester project can pay for itself in 6.5 years without subsidy. [ABSTRACT FROM AUTHOR]