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The world’s waste deposit has become more important due to the growth of the world economy and demography. Biomethanation is method of choice for the treatment of fermentable fractions of municipal waste. However, low methane yield due to operational issues and process instability prevent this technology from being widely applied. It shows that biogas is made up of approximately 50-70% CH4; 30-50% CO2 from traces of other gases. Solutions for improving CH4 production include controlling anaerobic digestion factors. These factors are essentially: the methanogenic potential, the carbon/nitrogen ratio (C/N), temperature, Hydrogen potential (pH) and tightness of the reactor. Among these factors, one of the most important, the pH, is analysed in this article in order to ensure production’s optimization. 02 substrates are studied potato peel and cow’s stomach waste: As results, the production of biogas varies constantly with the variation of pH. With a pH lower than 5 the production decreases. The best productions were obtained pH value between 6.9 and 7.4. That said, it is important to find a way to stabilize the pH variation in the production of biogas for better sustainability. Eggshell has been studied in order to maintain the balance and good results have been obtained.

The use of Information and Communication Technology (ICT) in the energy sector is becoming increasingly common. From data collection, recording and dissemination of energy information to design, planning and management of energy systems, scientific computing skills and tools, amount them programming languages. Depending on the problem to be solved, the choice of programming language is often very decisive for achieving the expected results. This article presents an analysis of the state of art of programming languages used in the energy domain and propose a case study of the programming languages to be used depending on the problem to be solved renewable energies sector. Programming languages for scientific computing are divided into two main classes: General Purpose Languages (GPL) and Domain Specific Languages (DSL); knowing a GPL make one better at using DSL. The analysis carried out shows that in energy engineering, the most used GPLs are C++ and python. Likewise, the most prominent DSLs are Julia, R and Matlab. The most suitable language for scientific computing, data science, visualization, and high-performance computing problems is Python. However, Julia which is in full expansion is not to be overlooked as it is dedicated to energy.

Many non-electrified rural communities find it challenging to follow a precise scientific protocol to assess the feasibility of installing a wind turbine on their site. The objective of the present work is to define a clear protocol for the assessment of wind potential of a rural site. The case of Logone Birni (LB) is taken as a case study. For this purpose, a protocol, integrating the wind parameters of the site have been used to calculate the wind power density, annual energy yield, and capacity factors at 10, 30, and 50 m height using 15 years data. The wind frequency distribution including seasonal has been investigated to determine accurately the wind power of the site. The coefficient of variation is calculated at three different heights. Also, an economic assessment per kWh of energy has been carried out. The results of this study show that it is possible to install a wind farm in LB site with a minimum of 30m height. In addition, wind turbines with a starting speed of 1.5 m/s and a rated power of 20 kW will produce electricity at a low cost (0.453USD/kWh).

Biogas is an ideal fuel for sustainable energy and an alternative to wood energy in sub-Saharan Africa. Its valorisation in rural areas is limited to cooking despite its enormous potential for electricity production. However, the inaccessibility of biogas analyzers to assess the quality of biogas and trigger its use in an engine is one of the main obstacles to this new trend. The aim of this work is to develop a simple and inexpensive portable digital device capable of analysing any biogas together with its production parameters. To achieve this, a prototype analyser using an Arduino card fitter with sensors was designed and built, then experimented with biogas produced from cow dung. Sampling tests were carried out after 4 and 10 days of loading 10-liter bioreactors with cow dung. A Better flammable biogas production was recorded at 10 days of loading with 66.41% methane (CH4), 32.43% CO2, and 1.14% water vapour (H2O). This biogas analyser was quite efficient and could identify gases produced by the methanization process while controlling the bioconversion parameters. Nonetheless, the biogas obtained can be purified for more efficient use.

Long-term cell performance is very sensitive to the cell operating temperature, and cell storage capacity can degrade quickly, if the temperature is not maintained within a narrow range (25–50 °C) during charging and discharging of (solar) batteries [1]. Efforts are recently being dedicated to developing models that seek to provide insights into that issue. However, not all models consider the operation of the photovoltaic (PV) battery storage system with regard to battery optimization and temperature effects. The present work provides a controllable algorithm to help charge controllers provide exact amount of PV electricity (charge equalization) to batteries with temperature compensation included, and a proposed charging and discharging schedules of the battery storage. The temperature compensated duty cycle for charging is modelled and the pulse-width modulation (PWM) signal is programmed to change with temperature following this duty cycle model. This research work is based on the optimization of solar battery storage where the micro controller-based charge controller enhances battery life by monitoring the temperature and controlling charging voltages and float charging voltages for specific temperatures. A buck converter was simulated in Proteus, and then realized in the laboratory. The duty cycle of the buck converter was adjusted with temperature. Results collected from lab experiments were plotted on MatLab and it shows homogeneity with calculated results. Moreover, battery-charging currents, battery direct current (DC) disconnect and battery switching for charging and discharging were performed for the converter. Future work is to extend this study to large-scale solar photovoltaic systems in order to overcome the operation limits encountered.

The considerable expansion of telecommunications infrastructure in non-electrified areas has led to massive consumption of non-renewable energy sources by diesel generators. The promotion of renewable energy technologies as an alternative to these diesel generators is until now confronted with the problems of Capital Expenditures and system reliability. The hybridization of fossil fuels with renewable energies would make it possible to find a better quality/cost/environment ratio for the supply of off-grid telecommunication base stations (BSs).This paper presents the analyses of eight different hybrid energy systems dedicated for telecommunications equipment with a BS antenna as case study. The techno-economic analysis of hybrid energy system comprises solar, wind and the existing power supply. All the necessary modelling, simulations, and techno-economic evaluations are carried out using the assessment software package HOMER (Hybrid Optimization Model for Electric Renewable). The result shows that adding solar PV to the existing system is the optimal option. For the site studied powered by grid and diesel generator, the hybrid PV-diesel-grid with storage battery system is the best optimal system configuration for the chosen antenna with an initial capital cost of 34,619$, a Net Present Cost (NPC) of 55,588$ and a Cost of Energy (COE) of 0.39$/kWh.

The main purpose of this work was to assess the influence of pH variation in biogas production and to study the effect of certain substrates (other than chemical products) on stabilizing production. Two substrates were used in production: banana peels (BP) and rumen scraps (RS). Five different mixing ratios were studied at a mesophilic temperature (35±2 °C): 25% RS+75% BP; 100% RS; 100% BP; 50% RS+50% BP and lastly 75% RS+25% BP. Substrate characterization parameters such as dry matter content, volatile dry matter content, pH, nitrogen-carbon ratio, biogas production and methane content were determined. A comparative study of the pH stabilization was then conducted using the mixtures (co-digestion) that obtained the best production results. Stabilizers included in particular potassium hydroxide (KOH), cocoa pods and empty Palmyra palm bunches. Biogas production varies with pH fluctuation. Indeed, it decreases whenever the pH value tends towards acidity. The best production was obtained at a pH of between 7.1 and 7.5. The use of cocoa pods as a pH stabilizer instead of KOH (chemical product) improved production by 24.48% and 0.11%, respectively, for the mixtures of 50% RS+50% BP and 25% RS+75% BP. Compared to KOH, empty Palmyra palm bunches had no positive impact. The findings showed that cocoa pods made it possible to stabilize the pH fluctuation and thus improve production performance. This improvement showed that cocoa pods could be used as a new stabilizer in anaerobic digestion systems. [ABSTRACT FROM AUTHOR]

Sub-Saharan Africa's low access to electricity and high vulnerability to climate change can be anticipated to constrain the region's future human and economic development prospects. The need for energy conservation, especially electricity, is of crucial importance as it is an economic solution to the problem of energy shortage and atmospheric carbon reduction. The role of Artificial intelligence (AI) has also been displayed by researchers in the promotion of energy management. Most of the past literature in the line of energy management strategies proposed various energy management models based on smart grid and smart meter technology, demand side management, home energy management schemes and management based on AI. This paper proposes an AI-based energy management for households in Nigeria. Genetic algorithm was used on smart meter-like data to optimize the energy consumption of households for 24 hours on a weekday and weekend. To achieve this aim, we determine the typical load profile of a mini-grid setting (for household and commercial load profile), develop a simulation of smart meter-like data and develop an energy management system to optimize electricity consumption during a weekday and weekend in a household. We corroborate our theoretical model with numerical results showing the energy (and consumption) saved during these periods. The algorithm will assist electricity consumers in rural communities to effectively manage their usage by avoiding wastage and the unnecessary payment for energy waste. [ABSTRACT FROM AUTHOR]