- Environmental Engineering, Civil Engineering Department, University of Granada (UGR), Granada, Spain 2011-2015.
-M.Sc. in Environmental Engineering, Environmental Engineering Department, Zagazig University (ZU), Zagazig, Egypt 2006-2010.
- B.Sc. in Civil Engineering, Civil Engineering Department, Faculty of Engineering, ZU, Zagazig, Egypt 2000-2005
RESEARCH INTERESTS
Solid waste management and treatment, biomass pellets and its application to produce energy via anaerobic digester or safe combustion, generation of electric power via solar and wind energy systems, climate change, and environmental impact assessment studies.
Environmental and Energy Performance of Rice Straw-Based Energy Pathways in Egypt: Life Cycle Assessment and Supply Chain Optimization Noha Said, Mahmoud M. Abdel-Daiem, Yasser A. Almoshawah, Amany A. Metwally, Noha A. Mostafa Sustainability Switzerland, 2026 This study investigates the environmental and energy performance of rice straw-based energy pathways in Egypt, combining life cycle assessment (LCA) with supply chain optimization to improve system efficiency. The analysis covers thirteen governorates producing over 4.45 million tons of rice straw annually. It examines the whole supply chain from paddy farming, straw collection, and transport to electricity generation and ash disposal. Total energy consumption was 11,287 TJ, dominated by farming (5673 TJ) and transport (5490 TJ). Greenhouse gas (GHG) emissions were estimated at 12,007.5 million kg CO2-eq, with significant contributions from farming (5158 million), combustion (3630 million), and natural gas use (3039 million). Gross electricity output was 5525 GWh, yielding a net of 4973 GWh, equivalent to 1116.5 kWh per ton of straw. Scenario analysis highlighted that the optimized multi-hub system, prioritizing Cluster 1 in the Nile Delta, which contributes over 92% of straw production and 4607 GWh of net electricity, achieved a reduction of more than 25% in transport distances and an 18% decrease in diesel consumption and related emissions. Sensitivity analysis further indicated that delivered electricity and GHG intensity are more sensitive to conversion efficiency and transmission and distribution losses than to moderate changes in transport assumptions. In addition to environmental improvements, the optimized scenario indicates potential social co-benefits, including rural employment generation, additional income opportunities for farmers, and improved air quality associated with reduced open-field burning. These outcomes are presented as indicative qualitative insights. Findings confirm rice straw as a strategic, scalable, and sustainable energy resource aligned with Egypt’s Vision 2030 and the UN Sustainable Development Goals (SDGs).
Techno-Economic and Sustainability Assessment of a Circular Two-Stage Olive Mill Wastewater Treatment System Using Olive Pomace-Derived Activated Carbon Raid Alrowais, Mahmoud M. Abdel daiem, Rania Saber Yousef, Osama konsowa Ahmed, Amany A. Metwally, et al. Bioresources, 2026 This study presents a sustainable and cost-effective approach for treating olive mill wastewater (OMWW) using activated carbon derived from olive pomace, a major by-product of olive oil production. The proposed system integrates the production of this adsorbent with wastewater treatment in a two-stage, circular process that combines acid precipitation and adsorption. The prepared activated carbon exhibited a well-developed porous structure and high iodine value (948 mg/g), enabling efficient removal of phenolic compounds from OMWW. The results showed removal efficiencies exceeding 99% for phenolic compounds, along with significant reductions in key pollutants, including chemical oxygen demand and total organic carbon. It was hypothesized that integrating waste valorization with wastewater treatment can enhance both environmental and economic performance. The findings confirmed this hypothesis, demonstrating high treatment efficiency and substantial cost reduction through process optimization, including reduced adsorbent dosage and reuse. Process optimization, including reduced adsorbent dosage and reuse, led to a substantial decrease in treatment costs. Overall, this study demonstrated that integrating waste valorization with wastewater treatment offers an effective and practical solution for environmental management. The findings highlight the potential of olive pomace-derived activated carbon as a low-cost and sustainable adsorbent for large-scale applications.
Hydrogeochemical and GIS-Integrated Evaluation of Drainage Water for Sustainable Irrigation Management in Al-Jouf, Saudi Arabia Raid Alrowais, Mahmoud M. Abdel-Daiem, Mohamed Ashraf Maklad, Wassef Ounaies, Noha Said Water Switzerland, 2026 This study evaluates the quality and irrigation suitability of drainage water in the Al-Jouf Region, Saudi Arabia, where water scarcity necessitates the reuse of nonconventional resources. Eighteen drainage water samples were analyzed for physicochemical parameters and irrigation indices, including electrical conductivity (EC), sodium percentage (Na+%), sodium adsorption ratio (SAR), magnesium hazard (MH), Kelly’s ratio (KR), permeability index (PS), and irrigation water quality index (IWQI). Multivariate statistical tools were applied to identify dominant hydrogeochemical processes. Inverse Distance Weighting (IDW) interpolation in ArcGIS Desktop 10.8 was employed to map significant physicochemical data and irrigation indicators. Results revealed that while EC values indicated low to moderate salinity (0.74–25.2 μS/cm), most samples showed high Na+%, SAR, and KR, classifying them as doubtful to unsuitable for irrigation. The IWQI ranged from 84.47 to 1617.87, indicating poor to inferior quality due to evaporation, fertilizer leaching, and sodium accumulation. Furthermore, the results highlight the importance of precise geographic modeling in determining whether drainage water is suitable for long-term agricultural use in arid regions such as Al-Jouf. Sustainable reuse of such drainage water requires freshwater blending, gypsum application, and the cultivation of salt-tolerant crops, aligning with Saudi Vision 2030 objectives for sustainable water management in arid regions.
Mathematical Modeling and Machine Learning Approaches for Biogas Production from Anaerobic Digestion: A Review Osama H. Galal, Mahmoud M. Abdel-Daiem, Hani S. Alharbi, Noha Said Bioresources, 2025 Anaerobic digestion (AD) is a widely recognized method for converting organic waste into biogas, offering a sustainable solution for both waste management and renewable energy generation. This review critically examines recent advancements in mathematical modeling and machine learning (ML) approaches applied to biogas production from AD processes. The study categorizes the models into daily and cumulative biogas production models, kinetic models, and hybrid AI-based predictive techniques. Special attention is given to the comparative evaluation of first-order kinetics, modified Gompertz, and Chen-Hashimoto models, highlighting their applicability and limitations. Furthermore, the integration of artificial neural networks (ANNs) and other ML algorithms is discussed in the context of optimizing biogas yield, understanding system dynamics, and reducing operational uncertainties. Research gaps are identified, including the need for more robust hybrid models, real-time monitoring systems, and studies under diverse feedstock and environmental conditions. The review emphasizes that combining traditional modeling with intelligent systems offers a powerful approach to enhancing AD performance and scaling sustainable energy solutions.
Sustainable Management of Wastewater Sludge Through Co-Digestion, Mechanical Pretreatment and Recurrent Neural Network (RNN) Modeling Raid Alrowais, Mahmoud M. Abdel-Daiem, Basheer M. Nasef, Amany A. Metwally, Noha Said Sustainability Switzerland, 2025 This study investigates the combined effect of wheat straw particle size and mixing ratio on the anaerobic co-digestion (ACD) of waste activated sludge under mesophilic conditions. Ten batch digesters were tested with varying straw-to-sludge ratios (0–1.5%) and particle sizes (5 cm, 1 cm, and <2 mm). Fine straw particles (<2 mm) at 1.5% loading achieved the highest removal efficiencies for TS (43.55%), TVS (47.83%), and COD (51.52%), resulting in a 140% increase in biogas yield and methane content of 60.15%. The energy recovery reached 14.37 kWh/kg, almost double the control. The developed Recurrent Neural Network (RNN) model (3 layers, 13 neurons, 500 epochs) predicted biogas production with 99.8% accuracy, a root mean square error (RMSE) of 0.0038, a mean absolute error (MAE) of 0.0093, and an R2 close to 1. These results confirm the potential of integrating agricultural residues into wastewater treatment for renewable energy recovery and emission reduction. This study uniquely integrates mechanical pretreatment of wheat straw with RNN-based modeling to enhance biogas generation and predictive accuracy. It establishes a dual-experimental AI framework for optimizing sludge–straw co-digestion systems. This approach provides a scalable, data-driven solution for sustainable waste-to-energy applications.
Bio-Waste to Bioenergy: Critical Assessment of Sustainable Energy Supply Chain in Egypt Noha Said, Raid Alrowais, Mahmoud M. Abdel-Daiem, Noha A. Mostafa Resources, 2025 This study analyses the potential electricity output from different bio wastes using various energy conversion technologies to enhance the share of renewable energy. Furthermore, it evaluates the carbon emissions mitigated by replacing fossil fuels with bioenergy, contributing to efforts to reduce environmental pollution. The findings reveal that Egypt’s annual biomass waste (BW) could total approximately 80 million tons, with the most significant contributions from agricultural crop residues and municipal solid waste (MSW). MSW incineration and crop residue combustion were found to have the highest power generation compared to other techniques. Additionally, the anaerobic digestion of various biomass types offers the benefits of lower greenhouse gas emissions while still generating significant energy. The electricity generation from different BW sources is approximately 49.14 TWh/year. This energy can be predominantly generated through direct combustion of agricultural crop residues (66%), incineration of MSW (29%), anaerobic digestion of sewage sludge (3%), and animal waste (2%). Furthermore, the reduction in carbon emissions from substituting fossil fuels with bioenergy is estimated at up to 30.47 million tons of CO2 annually, supporting efforts to mitigate climate change and combat global warming.
Adsorption of Lead (Pb(II)) from Contaminated Water onto Activated Carbon: Kinetics, Isotherms, Thermodynamics, and Modeling by Artificial Intelligence Badr Abd El-wahaab, Walaa H. El-Shwiniy, Raid Alrowais, Basheer M. Nasef, Noha Said Sustainability Switzerland, 2025 Heavy metals, extensively used in various industrial applications, are among the most significant environmental pollutants due to their hazardous effects on human health and other living organisms. Removing these pollutants from the environment is essential. In this study, activated carbon (AC) (Carbon C) was employed to eliminate Pb(II) from water. The optimal removal conditions were determined as follows: a 50 mg dose of activated carbon, an initial Pb(II) concentration of 100 mg/L, pH 4, a temperature of 30 °C, and a contact time of 60 min Under these conditions, activated carbon achieved a Pb(II) removal efficiency of approximately 97.86%. The adsorption data for Pb(II) closely aligned with the 2nd-order kinetic model, and the equilibrium data were effectively described by the Langmuir isotherm equation. The maximum adsorption capacity of Pb(II), as determined by the Langmuir model, was 48.75 mg/g. These methods were successfully applied to remove Pb(II) from various environmental and industrial wastewater samples. To accurately predict the percentage of Pb(II) removal based on parameters such as pollutant type, carbon dosage, pH, initial concentration, temperature, and treatment duration, feed-forward neural networks (FFNNs) were utilized. The FFNN model demonstrated outstanding predictive accuracy, achieving a root mean square error (RMSE) of 0.03 and an R2 value of 0.996.
Effect of Alkaline Pretreatment on the Characteristics of Barley Straw and Modeling of Methane Production via Codigestion of Pretreated Straw with Sewage Sludge Raid Alrowais, Mahmoud M. Abdel daiem, Ahmed M. Helmi, Basheer M. Nasef, Ananda Rao Hari, et al. Bioresources, 2024 Straw pretreatment enhances the cellulose accessibility and increases the methane yield from anaerobic digestion. This study investigated the effects of alkali pretreatments with different chemical agents (NaOH, KOH, and Na2CO3) on the physicochemical and thermal characteristics of barley straw, as well as methane production from codigestion with sewage sludge. Artificial neural network modeling with a feedforward neural network (FFNN) and slime mold optimization (SMO) techniques were used to predict methane production. NaOH pretreatment was shown to be the best pretreatment for removing hemicellulose and lignin and for increasing the cellulose accessibility. Moreover, there was a 2.57-fold higher level of methane production compared to that from codigestion with untreated straw. The removal ratios for the total solids, volatile solids, and chemical oxygen demand reached 59.3, 67.2, and 73.4%, respectively. The modeling results showed that the FFNN-SMO method can be an effective tool for simulating the methane generation process, since training, validating, and testing produced very high correlation coefficients. The FFNN-SMO accurately predicted the amount of methane produced, with an R2 of 0.998 and a 3.1×10-5 root mean square error (RMSE).
