Vermicompost production monitoring and the internet of things

This article presents the results obtained by implementing an application with an architecture based on the Internet of Things, applied to the production of red California earthworm, using an interface with which it is possible to monitor and collect data on temperature, humidity and PH with sensors that allow recording the necessary data. For the implementation, an experimental module was built in which the temperature and humidity variables were monitored, with the data obtained from the sensor measurements, the constant changes in temperature (between 20 to 29 ºC) and humidity (from 35% to 50%) were observed, This information made it possible to keep a weekly plan in which irrigation, aeration and compost mixing were attended to in a timely manner, reducing time, cost and human labor in the production of the red California earthworm and maintaining the reproduction of the red California earthworm in optimal conditions.

Technological and socio-economic study of akandji, a neglected traditional foodstuff made from corn (Zea mays L.) in Benin

Foodcrafts, active in Benin, offer a variety of products including akandji. It is a traditional bread made of corn consumed in South Benin. This work aimed to achieving a technological and socio-economic study related to akandji production and marketing activities in Benin. To do this, the methodology adopted was to conduct a pre-survey and a survey in the form of semi-structured interviews based on a questionnaire in the communes of Abomey, Bohicon and Ouidah (Pahou). After that, production monitoring was carried out with the three oldest akandji producers. The results showed that the production and sale of akandji were secular, exclusively female activities practised by women from Fon socio-cultural and sociolinguistic group. The profit per kg received by akandji producers in Abomey (266 XOF) was similar to that received by akandji producers in Pahou (256 XOF). The daily receipts for weekends and holidays were higher than those for working days in the survey localities. Furthermore, the results of the technological study showed that akandji manufacturing process in Abomey differs from that of Pahou. This process in Abomey involved the malting operation unlike that of Pahou. Fermentation times (12h), cooking times (1h) and production times (6 days) in Abomey exceeded fermentation times (1h30min), cooking times (45min) and production times (6h) in Pahou. In contrast, the fermentation (27oC) and cooking (100 oC) temperatures of akandji at Abomey were lower than those of fermentation (31oC) and cooking (178oC) in Pahou. The production of akandji is a profitable activity that strengthens the social status of the producer and ensures important socio-community functions by providing an appropriate local food for traditional rites and festivals and maintains sales markets firmly rooted in society.

Evaluation of Machine Learning Methods for Prediction of Multiphase Production Rates

Multiphase flow metering is an important tool for production monitoring and optimization. Although there are many technologies available on the market, the existing multiphase meters are only accurate to a certain extend and generally are expensive to purchase and maintain. Virtual flow metering (VFM) is a low-cost alternative to conventional production monitoring tools, which relies on mathematical modelling rather than the use of hardware instrumentation. Supported by the availability of the data from different sensors and production history, the development of different virtual flow metering systems has become a focal point for many companies. This paper discusses the importance of flow modelling for virtual flow metering. In addition, main data-driven algorithms are introduced for the analysis of several dynamic production data sets. Artificial Neural Networks (ANN) together with advanced machine learning methods such as GRU and XGBoost have been considered as possible candidates for virtual flow metering. The obtained results indicate that the machine learning algorithms estimate oil, gas and water rates with acceptable accuracy. The feasibility of the data-driven virtual metering approach for continuous production monitoring purposes has been demonstrated via a series of simulation-based cases. Amongst the used algorithms the deep learning methods provided the most accurate results combined with reasonable time for model training.

Intelligent Production Monitoring with Continuous Deep Learning Models

Monitoring of production rates is essential for reservoir management, history matching, and production optimization. Traditionally, such information is provided by multiphase flow meters or test separators. The growth of the availability of data, combined with the rapid development of computational resources, enabled the inception of digital techniques, which estimate oil, gas, and water rates indirectly. This paper discusses the application of continuous deep learning models, capable of reproducing multiphase flow dynamics for production monitoring purposes. This technique combines time evolution properties of a dynamical system and the ability of neural networks to quantitively describe poorly understood multiphase phenomena and can be considered as a hybrid solution between data-driven and mechanistic approaches. The continuous latent ordinary differential equation (Latent ODE) approach is compared to other known machine learning methods, such as linear regression, ensemble-based model, and recurrent neural network. In this work, the application of Latent ordinary differential equations for the problem of multiphase flow rate estimation is introduced. The considered example refers to a scenario, where the topside oil, gas, and water flow rates are estimated using the data from several downhole pressure sensors. The predictive capabilities of different types of machine learning and deep learning instruments are explored using simulated production data from a multiphase flow simulator. The results demonstrate the satisfactory performance of the continuous deep learning models in comparison to other machine learning methods in terms of accuracy, where the normalized root mean squared error (RMSE) and mean absolute error (MAE) of prediction below 5% were achieved. While LODE demonstrates the significant time required to train the model, it outperforms other methods for irregularly sampled time-series, which makes it especially attractive to forecast values of multiphase rates.

Experience of Application of Different Multiphase Metering Technologies for Cold Production and High Viscosity Oil Systems

Production monitoring is essential not only for fiscal applications, but also for production optimization and efficient reservoir management. So, production measurements must be both accurate and frequent enough, revealing a consistent trend of well operating parameters. This is especially important for reservoirs of complex geology, like oil rim reservoirs in poorly consolidated sandstone formations with presence of aquifer and gas cap drive. Production monitoring can be implemented with different technologies, accuracy of monitoring is however affected by different factors like gas content, viscosity and temperature of produced fluids. Paper presents pragmatic approach and analysis of applicability of different measurement technologies: compact two-phase separator and two different multiphase metering technologies applied at oil wells of Tazovskoye field operated by LLC "Meretoyakhaneftegaz", which production conditions are very challenging due to high gas volume fraction of the produced fluid, high viscosities and low temperatures.

Experimental Investigations of Offshore Sand Production Monitoring Based on the Analysis of Vibration in Response to Weak Shocks

Sand production is a problem that is often encountered in unconventional oil and gas exploitation and that is difficult to effectively solve. Accurate online monitoring of sand production is one of the keys to ensuring the safety and long-term production of oil wells as well as efficient production throughout the life cycle of production wells. This paper proposes a method for monitoring sand production in offshore oil wells that is based on the vibration response characteristics of sand-carrying fluid flow impinging on the pipe wall. This method uses acceleration sensors to obtain the weak vibration response characteristics of sand particles impinging on the pipe wall on a two-dimensional time-frequency plane. The time-frequency parameters are further optimized, and the ability to identify weakly excited vibration signals of sand particles in the fluid stream is enhanced. The difference between the impact response of the sand particles and the impact response of the fluid flow to the pipe wall is identified, and corresponding indoor verification experiments are carried out. Under different sand contents, particle sizes, and flow rates (sand content 0-2‰, sand particle size 96-212 μm, and flow velocity 1-3 m/s), the impact response frequency of sand particles to the pipe wall exhibits good consistency. The characteristic frequency band of sand impacting the pipe wall is 30-50 kHz. A statistical method is used to establish the response law of the noise signal of the fluid. Based on this knowledge, a real-time calculation model of sand production in offshore oil wells is constructed, and the effectiveness of this model is verified. Finally, a field test is carried out with a self-developed sand production signal dynamic time-frequency response software system on 4 wells of an oil production platform in the Bohai Sea. This system can effectively distinguish sand-producing wells from non-sand-producing wells. The dynamic time-frequency response, field test results, and actual laboratory results are consistent, verifying the effectiveness of the method proposed in this paper and further providing a theory for improving the effectiveness of the sand production monitoring method under complex multiphase flow conditions. This study also provides technical guidance for the industrial application of sand production monitoring devices in offshore oil wells.

Variables Governing Photosynthesis and Growth in Microalgae Mass Cultures

Since the 1950s, microalgae have been grown commercially in man-made cultivation units and used for biomass production as a source of food and feed supplements, pharmaceuticals, cosmetics and lately biofuels, as well as a means for wastewater treatment and mitigation of atmospheric CO2 build-up. In this work, photosynthesis and growth affecting variables—light intensity, pH, CO2/O2 exchange, nutrient supply, culture turbulence, light/dark cell cycling, biomass density and culture depth (light path)—are reviewed as concerns in microalgae mass cultures. Various photosynthesis monitoring techniques were employed to study photosynthetic performance to optimize the growth of microalgae strains in outdoor cultivation units. The most operative and reliable techniques appeared to be fast-response ones based on chlorophyll fluorescence and oxygen production monitoring, which provide analogous results.