Optimization via response surface methodology of palm kernel shell biochar for supplementary cementitious replacement.

The use of sustainable materials in the construction industry has been on the rise recently. Studies have proven that the use of conventional concrete and its raw materials has a negative impact on the environment. Research on incorporating biochar as a supplementary cementitious material has been recently evolving and has shown that the attributes of biochar are highly affected by the pyrolysis parameters. These attributes have enhanced the properties of biochar concrete and mortar composite. This paper identifies the different physiochemical properties exhibited by palm kernel shell biochar through optimization by response surface methodology. Focusing on some of the properties of biochar that have proven beneficial when used as a cement replacement. Very limited research has used optimization tools for the production of biochar with the intention of using it as a cement substitute. Pyrolysis was conducted by a tubular furnace at different temperature ranges from 200 °C to 800 °C. The biomass and biochar have been analyzed with TGA and FESEM-EDX. The targeted biochar properties and selected responses are the yield, carbon, oxygen, silica, and potassium content. The optimized parameters obtained are 409 °C, 15 °C/min, 120 min with responses of 38.2% yield, 73.37% carbon, 25.48% oxygen, 0.39% potassium and 0.44% silica. Thermal properties of the palm kernel shell biochar affected by the pyrolysis factors such as temperature, heating rate and residence time have also been discussed. In conclusion, this study supports and encourages the use of palm waste, which is abundant in Malaysia, as a supplementary cementitious material to promote sustainable growth in construction.

Stability of biochar derived from banana peel through pyrolysis as alternative source of nutrient in soil: feedforward neural network modelling study.

Biochar derived from banana peels can be used as an alternative nutrient in the soil that can promote crop growth while reducing fertiliser usage. Biochar stability has proportional relationship to biochar residence time in the soil and potassium is one of the vital nutrients needed for plant growth. This research aims at providing optimum pyrolysis operating conditions like temperature, residence time, and heating rate using banana peels as feedstock. An electrical tubular furnace was used to conduct the pyrolysis process to convert banana peels into biochar. The elemental compositions of biochar are potassium, oxygen (O), and carbon (C) content. The O:C ratio was used as the biochar stability indicator. Analysis of results showed that operating temperature has the most remarkable effect on biochar yield, biochar stability, and biochar's potassium content. In addition, a multilayer feedforward artificial neural network model was developed for the pyrolysis process. Eleven training algorithms were selected to model the multi-input multi-output neural network (MIMO). The most suitable training algorithm was identified through four performance criterions which are root mean square error (RMSE), mean absolute error (MSE), mean absolute percentage error (MAPE), and regression (R2). The results show that the Levenberg-Marquardt backpropagation training algorithm has the lowest error. From the chosen training algorithm, neural network was trained, and optimum operating parameters for banana peel were predicted at 490 °C, 110 min, and 11 °C/min with a high yield of 47.78%, O/C ratio of 0.2393, and 14.04 wt. % of potassium.

Biochar production via pyrolysis of citrus peel fruit waste as a potential usage as solid biofuel.

Renewable energy sources such as biomass have been proven to be one of the promising sustainable alternatives to fossil fuels. However, using biomass directly as a fuel is less attractive due to its high moisture content, poor grindability, low bulk density, and low energy density nature. Hence biomass can be converted into biochar to overcome these challenges. In this study, biochar was derived from citrus peels biomass by slow pyrolysis over the temperature range of 300-700 °C. The effect of pyrolysis temperature on the quality of citrus peels-derived biochar was examined based on the physical and chemical properties obtained from various analyses. The citrus peels biomass and biochar were characterized by means of higher heating value (HHV) analysis, field emission scanning electron microscopy with energy dispersive X-ray spectroscopy (FESEM-EDX), Fourier transform infrared ray (FTIR) analysis, proximate and thermogravimetric analysis. Based on the characterization results, the potential usage of the derived biochar as a solid fuel was discussed. Results obtained from the pyrolysis experiments indicated that a lower pyrolysis temperature produced a higher char yield. The carbon content and energy content of biochar were found to be increasing with pyrolysis temperature. Biochar produced at 500 °C presented the best fuel properties by having the highest value of HHV and carbon content. The results from this study provided great insights into biomass waste reutilisation to generate value-added biochar for renewable energy production in Malaysia.

Evaluation of potential feedstock for biogas production via anaerobic digestion in Malaysia: kinetic studies and economics analysis.

As the population increases, energy demands continue to rise rapidly. In order to satisfy this increasing energy demand, biogas offers a potential alternative. Biogas is economically viable to be produced through anaerobic digestion (AD) from various biomass feedstocks that are readily available in Malaysia, such as food waste (FW), palm oil mill effluent (POME), garden waste (GW), landfill, sewage sludge (SS) and animal manure. This paper aims to determine the potential feedstocks for biogas production via AD based on their characteristics, methane yield, kinetic studies and economic analysis. POME and FW show the highest methane yield with biogas yields up to 0.50 L/g VS while the lowest is 0.12 L/g VS by landfill leachate. Kinetic study shows that modified Gompertz model fits most of the feedstock with R 2 up to 1 indicating that this model can be used for estimating treatment efficiencies of full-scale reactors and performing scale-up analysis. The economic analysis shows that POME has the shortest payback period (PBP), highest internal rate of return (IRR) and net present value (NPV). However, it has already been well explored, with 93% of biogas plants in Malaysia using POME as feedstock. The FW generation rate in Malaysia is approximately 15,000 tonnes per day, at the same time FW as the second place shows potential to have a PBP of 5.4 years and 13.3% IRR, which is close to the results achieved with POME. This makes FW suitable to be used as the feedstock for biogas production.

How does the Internet of Things (IoT) help in microalgae biorefinery?

Microalgae biorefinery is a platform for the conversion of microalgal biomass into a variety of value-added products, such as biofuels, bio-based chemicals, biomaterials, and bioactive substances. Commercialization and industrialization of microalgae biorefinery heavily rely on the capability and efficiency of large-scale cultivation of microalgae. Thus, there is an urgent need for novel technologies that can be used to monitor, automatically control, and precisely predict microalgae production. In light of this, innovative applications of the Internet of things (IoT) technologies in microalgae biorefinery have attracted tremendous research efforts. IoT has potential applications in a microalgae biorefinery for the automatic control of microalgae cultivation, monitoring and manipulation of microalgal cultivation parameters, optimization of microalgae productivity, identification of toxic algae species, screening of target microalgae species, classification of microalgae species, and viability detection of microalgal cells. In this critical review, cutting-edge IoT technologies that could be adopted to microalgae biorefinery in the upstream and downstream processing are described comprehensively. The current advances of the integration of IoT with microalgae biorefinery are presented. What this review discussed includes automation, sensors, lab-on-chip, and machine learning, which are the main constituent elements and advanced technologies of IoT. Specifically, future research directions are discussed with special emphasis on the development of sensors, the application of microfluidic technology, robotized microalgae, high-throughput platforms, deep learning, and other innovative techniques. This review could contribute greatly to the novelty and relevance in the field of IoT-based microalgae biorefinery to develop smarter, safer, cleaner, greener, and economically efficient techniques for exhaustive energy recovery during the biorefinery process.

Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) peel.

Presence of copper within water bodies deteriorates human health and degrades natural environment. This heavy metal in water is treated using a promising biochar derived from rambutan (Nephelium lappaceum) peel through slow pyrolysis. This research compares the efficacies of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models and evaluates their capability in estimating the adsorption efficiency of biochar for the removal of Cu (II) ions based on 480 experimental sets obtained in a laboratory batch study. The effects of operational parameters such as contact time, operating temperature, biochar dosage, and initial Cu (II) ion concentration on removing Cu (II) ions were investigated. Eleven different training algorithms in ANN and 8 different membership functions in ANFIS were compared statistically and evaluated in terms of estimation errors, which are root mean squared error (RMSE), mean absolute error (MAE), and accuracy. The effects of number of hidden neuron in ANN model and fuzzy set combination in ANFIS were studied. In this study, ANFIS model with Gaussian membership function and fuzzy set combination of [4 5 2 3] was found to be the best method, with accuracy of 90.24% and 87.06% for training and testing dataset, respectively. Contribution of this study is that ANN, ANFIS, and MLR modeling techniques were used for the first time to study the adsorption of Cu (II) ions from aqueous solutions using rambutan peel biochar.