The research involved the use of a hybrid research design that included experimental work, process simulation and computerized design involving sampling and laboratory studies, modeling using BioWin, post-processing using MATLAB, energy recovery estimation, sensitivity analysis, economic analysis, and greenhouse gas emission analysis. BioWin software was adopted for process modeling while MATLAB was used for computations and post-processing.

This research was carried out in Aladinma and Ikenegbu wastewater treatment plants found within Owerri Municipal Area, Imo State, Nigeria. These plants were chosen because they are typical municipal wastewater treatment plants ideal for the investigation of the feasibility of renewable energy recovery from wastewater sludge.

The area is located between latitude 5˚28' and 5˚31'N, and longitudes 7˚00' and 7˚04'E and occupies an approximate land area of 551 km². The area has a tropical wet climate with average annual rainfall and temperature of about 2,250 mm and 27˚C respectively. Owerri Municipal has a population of 127,213 inhabitants with about 17,000 households including shops and offices.

The people of Owerri have predominantly an urban nature with involvement in civil service, trading, business, and other ventures. The Rivers that drain the city are Otamiri and Nworie Rivers. The city has several tertiary institutions. The wastewater treatment plants selected as the sites of study include residential, institutional and commercial areas from which representative wastewater/sludge and shown in Table 1.

Table 1.Two selected WWTPs representative.

Operational data of WWTPs used for biogas production assessment is contained in Table 2.

Table 2.Operational data of the selected WWTPs.

Samples of wastewater/sludge were collected from selected sampling locations at Aladinma Wastewater Treatment Plant (Plant A) and Ikenegbu Wastewater Treatment Plant (Plant B) of Owerri Municipal, Imo State, Nigeria. Sampling was done over a period of seven (7) months from June to December 2025 to provide variations in the characteristics of wastewater/sludge under varied operating conditions.

Samples were collected from the specified locations within each of the treatment plants and duly labeled upon collection. The samples were (grab/composite) samples, and were collected in clean sampling bottles. After sample collection, they were preserved using laboratory procedures, transferred to the laboratory, and characterized following standard methods for wastewater/sludge characterization [8][9]. Sampling was done to provide representative samples from both plants for physicochemical analysis and further Bio-Win modeling and biogas production evaluation. The parameters used as the primary input data introduced into the Bio-Win simulation software for modeling the anaerobic digestion and wastewater treatment processes is presented in Table 3.

Table 3.Input data for BioWin simulation.

The main variables analyzed were:

Chemical Oxygen Demand (COD);Biochemical Oxygen Demand in five days (BOD₅);Total Suspended Solids (TSS);Volatile Suspended Solids (VSS).

COD is used as an indicator of the amount of oxidizable organic substances, while BOD₅ stands for the biodegradable part. TSS stands for the total suspended solids, while VSS is the organic part of suspended solids. These parameters were chosen because the amount and biodegradability of organic substances affect the anaerobic digestion and methane production [10][11].

The obtained wastewater characteristics were introduced in BioWin along with some other plant operation parameters such as the amount of influent, COD concentration, temperature, retention time and OLR. Models were built separately for both plants—Aladinma and Ikenegbu treatment plants. BioWin was used as the main process simulation software in order to make a model of anaerobic digestion and biogas and methane production estimates. The model includes the main stages of anaerobic digestion including hydrolysis, acidogenesis, acetogenesis and methanogenesis. The results include the amount of produced biogas and the concentration of methane. Bio-Win has been used in the modeling of wastewater and anaerobic processes [12][13].

The flowsheet arrangement of the Wastewater Treatment Plant at Owerri is shown in Figure 1Figure 1. Simulations of the plant were carried out after completion of the flowsheet arrangement until convergence was attained. Different components of the WTP model were employed in the construction of the plant model as follows:

a) Influent input stream for plant model parameters such as flow rate, BOD, and Total Suspended Solids (TSS).

b) Primary Clarifier for separation efficiency and sludge ratio.

c) Aeration tank for biological oxidation (ASM1).

d) Secondary Clarifier for sludge thickening and recycling.

e) Anaerobic digester for ADM1 model kinetics operated at 32˚C - 33˚C.

f) Gas Collector for biogas and methane generation.

g) Pipe component to connect components in series.

Figure 1. Bio-Win model of wastewater treatment plant.

Data on the performance of biogas production and sludge digestion specific to plant operations was not available for the period of June to December 2025. It means that there would be no validation of Bio-Win prediction by field data. All values of biogas, methane, and energy recovery presented in the paper are considered scenario-based, rather than verified prediction. Lack of the field data is a research limitation.

MATLAB software was applied as an auxiliary computing tool. Outputs of Bio-Win were processed in MATLAB for further numerical computation, visual presentation, and comparison and sensitivity analysis.

The MATLAB software was used to:

Plot dynamics of biogas and methane production;compare the results of Aladinma and Ikenegbu;Analyze influence of temperature and COD;estimate retention time and OLR;compute methane production;assess electrical energy recovery;estimate possible saving in electricity cost;estimate payback period; andassess reduction of greenhouse gases.

Thus, MATLAB provided an extra flexibility in analyzing, while Bio-Win model was employed as main anaerobic digestion process-based model.

The sensitivity analysis was performed by changing the values of temperature, influent COD, HRT/SRT, and OLR from their base-line Bio-Win operating conditions. The sensitivity measure was the percent change in methane generation from the base line case, as shown below.

The range of values and increment was used for the sensitivity analysis.

Energy recovery potential was evaluated based on simulated biogas production and its methane fraction [14][15]. Methane fraction was regarded as the main component bearing energy. Electric energy recovered per day was estimated using energy content of methane and energy conversion efficiency. Annual energy recovery was then computed as:

Economic evaluation was conducted using an assumed electricity tariff = 120/kWh.

Annual electricity savings were calculated using equation below:

where:

S = annual electricity-cost savings, E_annual = annual electricity recovery, T = electricity tariff, C = initial investment cost

The simple payback period was estimated from:

The environmental impact assessment was related to greenhouse gas reductions due to methane capture and use. The reduction estimate is about 574 tonnes CO₂-e per year. The biogas recovery can be used environmentally in terms of reducing the methane emissions and electricity generation using conventional energy sources [16]-[20].

The BioWin simulation predicted approximately 700 m³/day of biogas from Aladinma Treatment Plant and 850 m³/day from Ikenegbu Treatment Plant (See Table 4).

Table 4. Simulated biogas production.

Ikenegbu produced about 150 m³/day more biogas than Aladinma. This corresponds to about 21.4% higher production compared to Plant A. These differences could result from differences in the amount of water treated, COD concentration, organic loading, sludge properties and other factors. The obtained results show that both wastewater treatment plants have significant opportunities for recovering energy using anaerobic digestion.

Assumptions for Electricity Recovery from Methane

The calculation of the electricity-recovery potential from the simulated methane production was carried out under normal engineering assumptions. The LHV of methane was assumed to be 9.97 kWh/m³ CH₄. The electrical efficiency of the generator gas engine was assumed to be 35%. Parasitic energy of 10% was used to consider electricity use in sludge treatment, pumping, and gas conditioning. The availability of the plant was considered 90%, while gas-cleaning loss was 5% before electricity production. The electricity-recovery net was thus calculated considering the simulated methane production and gas-cleaning loss, generator efficiency, parasitic energy use, and plant availability (See Table 5).

Table 5.Assumptions for electricity recovery from methane.

The net electricity that could therefore be estimated for a production of V_CH4 m³/day would be:

where, the final 0.90 represents the 10% parasitic-energy allowance.

The methane content in the simulated biogas was about 60.5%. Methane is the main constituent in the biogas containing energy and thus determines the energy value of the gas. The estimated specific methane yield was about: 0.16 - 0.19 m³ CH₄/kg COD removed. It can be concluded that a considerable part of organic material removed from the wastewater can be transformed into methane.

Temperature affects growth of microorganisms, hydrolysis, acidogenesis, acetogenesis and methanogenesis processes. Maintaining optimal temperature, thus, becomes important for effective anaerobic digestion. Temperature was found to have a high influence on methane production in the MATLAB sensitivity analysis as shown in Table 6.

COD concentration also had a high influence on methane production. Higher COD concentrations usually meant more substrate was available and thus higher methane production potential. Excessively high organic loading rate, however, could destabilize anaerobic digestion and cause accumulation of volatile fatty acids. This makes COD concentration consideration along with digester capacity and OLR important.

Retention time affected the level of substrate degradation and methane production. Too short retention time could cause incomplete biodegradation whereas too long retention time would increase digester volume and its cost without any energy gains. Retention time, therefore, should be optimized.

An increase in OLR will initially result in increased availability of substrates, hence increased biogas generation. Excessive OLRs can lead to instability and inhibit the methanogens. The above results show that an OLR range is required to generate maximum biogas production and a stable process.

Table 6 describes the one-way sensitivity analysis which was performed in order to determine the sensitivity of methane production to various important operational parameters. Temperature was allowed to vary from 25˚C to 35˚C, while the concentration of COD ranged from 300 mg/L to 700 mg/L. Retention time was changed from 10 days to 30 days, while OLR varied from 50% to 150% of the bioWin baseline. The steps sizes for the above parameters were 5˚C, 100 mg/L, 5 days, and 25% of the baseline, respectively. Sensitivity of the parameter was determined by calculating the percentage change in CH₄ production.

Table 6. Results of sensitivity analysis.

Complementary role was played by BioWin and MATLAB. BioWin was utilized to conduct the process-based anaerobic digestion modeling whereas MATLAB was employed for numerical analysis and sensitivity analysis (See Table 7).

Table 7.Comparison of BioWin and MATLAB results.

From the comparison, it is clear that Bio-Win provides the process model basis, whereas MATLAB offers analytical tools for supporting computation. The combined use of both approaches will help improve the understanding of the technical, economic and environmental aspects of biogas production from wastewaters.

The 3 - 5-year payback period was calculated considering the capital cost at which the biogas-powered electric generator was installed, which included costs related to the digester, gas collection and conditioning, storage, and the generator itself. The annual maintenance and operating cost was subtracted from the savings in electricity to arrive at the annual net benefit. The replacement of the generator was considered as one of the life-cycle costs.

The greenhouse gas assessment had a plant-gate boundary that included methane emissions, capturing, electrical power generation, and fugitive emissions. The methane emissions were then converted to CO₂ equivalents using the 100-year global warming potential from IPCC while the electricity generation was given credit for the avoidance of emissions from the grid.

Figure 2. Overall Bio-Win vs MATLAB performance comparison (Subplots).

Figure 2Figure 2 shows the comprehensive comparison of the methane yield, biogas generation, energy utilization and COD removal efficiency in both Bio-Win and MATLAB models. From the graph, it is clear that both models exhibit almost same performance tendencies in relation to all parameters under study. Bio-Win generates higher values compared to the MATLAB, which can be attributed to higher accuracy of the process simulation in Bio-Win, whereas MATLAB offers just a simple analytical approximation.

The combined energy recovery potential was estimated at approximately 3120 kWh/day.

Annual energy recovery was calculated as:

3120 × 365 = 1,138,800 kWh/year

Thus:

Annual electricity recovery ≈ 1.14 GWh/year.

Table 8.Energy-Recovery potential.

The recovery of energy from the sludge could satisfy about 39.5% of the total power consumption needs of the two water treatment facilities. This is a clear demonstration that wastewater sludge can be used to generate renewable energy (See Table 8).

At an electricity tariff of ₦120/kWh:

1,138,800 × 120 = ₦136,656,000/year

Thus, the annual cost savings in electricity = ₦136.7 million/year. The previously mentioned value of approximately ₦140 million/year could be seen as a rough estimate. The expected payback period of 3 - 5 years implies favorable economic conditions for the investment into biogas collection, treatment, and electricity generation systems. Project economics would depend on costs of construction, efficiency of the generator, maintenance of the system, cleaning of the gas, and others.

The analysis shows possible mitigation of greenhouse gases around 574 tonnes CO₂-eq./year. The environmental advantage is based on the capture of the methane that would otherwise have been released into the atmosphere for the generation of electricity. This confirms the importance of wastewater treatment within a circular energy system.

Comparative analysis of the two plants is displayed in Table 9 demonstrated the higher simulated biogas production. However, greater biogas volume does not necessarily indicate better wastewater-treatment performance. Treatment efficiency should also consider COD removal, solids stabilization, specific methane yield and energy consumption.

Table 9. Comparative performance.

This study analyzed and evaluated the potential for biogas production from selected wastewater treatment plants in Owerri Municipality using the combined experimental, BioWin and MATLAB approach. The characteristics of wastewater sludge from Aladinma and Ikenegbu treatment plants were obtained through COD, BOD₅, TSS and VSS to determine its suitability for anaerobic digestion.

The Biowin model indicated an estimated biogas generation potential of 700 m³/day for Aladinma and 850 m³/day for Ikenegbu, giving a combined total of 1550 m³/day. The average methane content was 60.5%, while the specific methane yield was 0.16 - 0.19 m³ CH₄/kg COD removed.

Temperature and influent COD concentration have been identified as the most sensitive parameters affecting methane production by the MATLAB model. The retention time and OLR parameters also affected the process, though they had less influence compared to the first two.

The combined potential electrical energy recovery was estimated to be 3120 kWh/day, which equals 1.14 GWh/year. This could potentially provide 39.5% of the combined power demands of the two treatment plants.

At an electricity cost of ₦120/kWh, the calculated energy-cost savings potential would be approximately ₦136.7 million/year, and the rounded estimate of ₦140 million/year would represent approximately the same order of savings. A simple payback period of 3 - 5 years implies economic feasibility, although this may depend on accurate project costing. A potential greenhouse gas mitigation of approximately 574 tonnes CO₂-eq/year was also estimated.

Generally, the results suggest that the wastewater sludge from the selected Owerri treatment plants can be a valuable source of renewable energy. The combined Biowin-MATLAB approach used in this study has proven to be a powerful tool for process modeling, sensitivity analysis, energy evaluation and decision making.

From the study, the following recommendations are proposed as follows:

The recovery system of biogas generation in Aladinma and Ikenegbu WWTPs should be examined for its feasibility.The temperature, COD, pH, OLR and retention time of the process should be continuously monitored and controlled to ensure that anaerobic digestion is consistent.Biogas should be cleaned properly and conditioned, especially removing moisture and hydrogen sulphide, before using the biogas in generating equipment.The combined heat and power (CHP) system should be analyzed when both electrical and thermal energies can be efficiently utilized.Techno-economic feasibility analysis should be conducted with the current cost structure for the equipment and facilities in Nigeria. Long-term data from the experiments should be obtained to improve the calibration and validation of the BioWin model.The co-digestion of the wastewater sludge with municipal waste for enhancing methane generation should be studied.The further study of the optimization of temperature, OLR and retention time of the digesters should be conducted using MATLAB-based optimization algorithms.The government and municipality should promote the wastewater resource recovery by developing proper policies and incentives.The method used should be adopted by other wastewater treatment plants in Nigeria to build a national database for energy production using waste water.