Modeling Methane Emission of Wastewater Anaerobic Pond at Palm Oil Mill Using Radial Basis Function Neural Network

Plant-based industries such as palm oil mills will generate wastewater rich in organic matter. Palm oil mill effluent (POME) treatment in Indonesia is still dominant with conventional methods without the capture of methane. This system does not know the value of methane emitted into the atmosphere. Measurement and testing of biomethane from anaerobic ponds of palm oil mills are relatively difficult because gas material is rapidly changing. An alternative methodology that is accurate through modeling with a radial basis function neural network (RBFNN) with abiotic variable input. The aim of this research is to find out an anaerobic pond methane emission model of POME and simulation to find out the dynamics of methane emissions. Methane emission data is measured by a TGS2611 methane gas sensor CH4-meter system and using closed static chambers. A sampling of wastewater and methane gas was conducted in October-November 2018. The results showed that the methane gas emission model was obtained in the AP with RBFNN. The best RBFNN model had a 5-5-3 network architecture, spread 0.11 and error-goals 0.0005, R 0.940652 and MSE 0.003166. The reliability of RBFNN in determining models with non-linear field data variables was quite good, which was influenced by the number of data patterns, types and accuracy of the variables, network architecture, and the ANN model used. The simulation and prediction of methane emissions in the lowest-moderate-highest variable value scenario found that the COD-R and VS-R variables greatly affected the anaerobic pond WWTP emissions of multiple feeding systems. Even so, inlet wastewater temperature and rainfall variables had not significantly affected methane gas emissions, because the temperature was in a mesophilic range (30-40 oC) and the effect of rainfall would depend mainly on the high-low levels of organic matter (COD and VS)

Municipal wastewater treatment with pond technology : historical review and future outlook

Facing an unprecedented population growth, it is difficult to overstress the assets for wastewater treatment of waste stabilization ponds (WSPs), i.e. high removal efficiency, simplicity, and low cost, which have been recognized by numerous scientists and operators. However, stricter discharge standards, changes in wastewater compounds, high emissions of greenhouse gases, and elevated land prices have led to their replacements in many places. This review aims at delivering a comprehensive overview of the historical development and current state of WSPs, and providing further insights to deal with their limitations in the future. The 21st century is witnessing changes in the way of approaching conventional problems in pond technology, in which WSPs should no longer be considered as a low treatment technology. Advanced models and technologies have been integrated for better design, control, and management. The roles of algae, which have been crucial as solar-powered aeration, will continue being a key solution. Yet, the separation of suspended algae to avoid deterioration of the effluent remains a major challenge in WSPs while in the case of high algal rate pond, further research is needed to maximize algal growth yield, select proper strains, and optimize harvesting methods to put algal biomass production in practice. Significant gaps need to be filled in understanding mechanisms of greenhouse gas emission, climate change mitigation, pond ecosystem services, and the fate and toxicity of emerging contaminants. From these insights, adaptation strategies are developed to deal with new opportunities and future challenges

Identifying four phytoplankton functional types from space: An ecological approach

Deriving maps of phytoplankton taxa based on remote sensing data using bio-optical properties of phytoplankton alone is challenging. A more holistic approach was developed using artificial neural networks, incorporating ecological and geographical knowledge together with ocean color, bio-optical characteristics, and remotely sensed physical parameters. Results show that the combined remote sensing approach could discriminate four major phytoplankton functional types (diatoms, dinoflagellates, coccolithophores, and silicoflagellates) with an accuracy of more than 70%. Models indicate that the most important information for phytoplankton functional type discrimination is spatio-temporal information and sea surface temperature. This approach can supply data for large-scale maps of predicted phytoplankton functional types, and an example is shown

A New Control and Design of PEM Fuel Cell Powered Air Diffused Aeration System

Aeration of water by using PEM fuel cell power is not only a new application of the renewable energy, but also, it provides an affordable method to promote biodiversity in stagnant ponds and lakes. This paper presents a new design and control of PEM fuel cell powered by diffused air aeration system for a shrimp farm in Mersa Matruh in Egypt. Also Artificial intelligence (AI) techniques control is used to control the fuel cell output power by controlling input gases flow rate. Moreover the mathematical modeling and simulation of PEM fuel cell is introduced. A comparison study is applied between the performance of fuzzy logic control (FLC) and neural network control (NNC). The results show the effectiveness of NNC over FLC

Deployment and control of adaptive building facades for energy generation, thermal insulation, ventilation and daylighting: A review

A major objective in the design and operation of buildings is to maintain occupant comfort without incurring significant energy use. Particularly in narrower-plan buildings, the thermophysical properties and behaviour of their façades are often an important determinant of internal conditions. Building facades have been, and are being, developed to adapt their heat and mass transfer characteristics to changes in weather conditions, number of occupants and occupant’s requirements and preferences. Both the wall and window elements of a facade can be engineered to (i) harness solar energy for photovoltaic electricity generation, heating, inducing ventilation and daylighting (ii) provide varying levels of thermal insulation and (iii) store energy. As an adaptive façade may need to provide each attribute to differing extents at particular times, achieving their optimal performance requires effective control. This paper reviews key aspects of current and emerging adaptive façade technologies. These include (i) mechanisms and technologies used to regulate heat and mass transfer flows, daylight, electricity and heat generation (ii) effectiveness and responsiveness of adaptive façades, (iii) appropriate control algorithms for adaptive facades and (iv) sensor information required for façade adaptations to maintain desired occupants’ comfort levels while minimising the energy use

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Three-Dimensional Short-Term Prediction Model of Dissolved Oxygen Content Based on PSO-BPANN Algorithm Coupled with Kriging Interpolation

Dissolved oxygen (DO) content is a significant aspect of water quality in aquaculture. Prediction of dissolved oxygen may timely avoid the financial loss caused by inappropriate dissolved oxygen content and three-dimensional prediction can achieve more accurate and overall guidance. Therefore, this study presents a three-dimensional short-term prediction model of dissolved oxygen in crab aquaculture ponds based on back propagation artificial neural network (BPANN) optimized by particle swarm optimization (PSO), which coupled with Kriging method. In this model, wavelet analysis is adopted for denoising, BPANN optimized by PSO is utilized for data analysis and one-dimensional prediction, and Kriging method is used for three-dimensional prediction. Compared with traditional one-dimensional prediction model, three-dimensional model has more real reaction of dissolved oxygen content in crab growth environment. In particular, the merits of PSO are evaluated against genetic algorithm (GA). The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for PSO model are 0.136445, 0.90534, and 0.15384, respectively, while for the GA model the values are 2.04184, 1.18316, and 0.21014, respectively. Furthermore, results of cross validation experiment show that the average error of this model is 0.0705 (mg/L). Consequently, this study suggests that the prediction model operates in a satisfactory manner