The aim of this study was to spur the lipid accumulation by larvae of Hermetia illucens or black soldier fly (BSFL) via feeding with yeast fermented medium. The Saccharomyces cerevisiae, a single cell yeast, was introduced at different concentrations (0.02, 0.1, 0.5, 1.0, 2.5 wt %) to execute an in-situ fermentation on coconut endosperm waste. The rearing of BSFL was started simultaneously and the rearing was stopped once the BSFL reached the fifth instar. With the increasing of yeast concentration, the rearing duration of BSFL was shortened from 15.5 to 13.5 days. Moreover, it was found that at 0.5 to 1.0 wt % yeast concentration, the lipid yield and lipid productivity of BSFL were statistically enhanced to their highest peaks, namely, at 49.4% and 0.53 g/day, respectively. With regard to biodiesel composition, BSFL-derived biodiesel contained mainly C12:0, C14:0, C16:0 and C18:1. The higher amount of saturated fatty acids could strengthen the oxidative stability biodiesel produced as compared with non-edible oils or microalgal lipid. At last, the addition of yeast was also found to improve the waste reduction index of coconut endosperm waste (CEW) from 0.31 to 0.40 g/day, heralding the capability of BSFL to valorize organic waste via bioconversion into its biomass to serve as a feedstock for biodiesel production.

Seabed logging (SBL) is an application of electromagnetic (EM) waves for detecting potential marine hydrocarbon-saturated reservoirs reliant on a source–receiver system. One of the concerns in modeling and inversion of the EM data is associated with the need for realistic representation of complex geo-electrical models. Concurrently, the corresponding algorithms of forward modeling should be robustly efficient with low computational effort for repeated use of the inversion. This work proposes a new inversion methodology which consists of two frameworks, namely Gaussian process (GP), which allows a greater flexibility in modeling a variety of EM responses, and gradient descent (GD) for finding the best minimizer (i.e., hydrocarbon depth). Computer simulation technology (CST), which uses finite element (FE), was exploited to generate prior EM responses for the GP to evaluate EM profiles at “untried” depths. Then, GD was used to minimize the mean squared error (MSE) where GP acts as its forward model. Acquiring EM responses using mesh-based algorithms is a time-consuming task. Thus, this work compared the time taken by the CST and GP in evaluating the EM profiles. For the accuracy and performance, the GP model was compared with EM responses modeled by the FE, and percentage error between the estimate and “untried” computer input was calculated. The results indicate that GP-based inverse modeling can efficiently predict the hydrocarbon depth in the SBL.

This work proposes a stochastic process-based inversion to estimate hydrocarbon resistivity based on multifrequency electromagnetic (EM) data. Currently, mesh-based algorithms are used for processing the EM responses which cause high time-consuming and unable to quantify uncertainty. Gaussian process (GP) is utilized as the alternative forward modeling approach to evaluate the EM profiles with uncertainty quantification. For the optimization, gradient descent is used to find the optimum by minimizing its loss function. The prior EM profiles are evaluated using finite element (FE) through computer simulation technology (CST) software. For validation purposes, mean squared deviation and its root between EM profiles evaluated by the GP and FE at the unobserved resistivities are computed. Time taken for the GP and CST to evaluate the EM profiles is compared, and absolute error between the estimate and its simulation input is also computed. All the resulting deviations were significantly small, and the GP took lesser time to evaluate the EM profiles compared to the software. The observational datasets also lied within the 95% confidence interval (CI) where the resistivity inputs were estimated by the proposed inversion. This indicates the stochastic process-based inversion can effectively estimate the hydrocarbon resistivity in the seabed logging.

The marine controlled-source electromagnetic (CSEM) technique is an application of electromagnetic (EM) waves to image the electrical resistivity of the subsurface underneath the seabed. The modeling of marine CSEM is a crucial and time-consuming task due to the complexity of its mathematical equations. Hence, high computational cost is incurred to solve the linear systems, especially for high-dimensional models. Addressing these problems, we propose Gaussian process (GP) calibrated with computer experiment outputs to estimate multi-frequency marine CSEM profiles at various hydrocarbon depths. This methodology utilizes prior information to provide beneficial EM profiles with uncertainty quantification in terms of variance (95% confidence interval). In this paper, prior marine CSEM information was generated through Computer Simulation Technology (CST) software at various observed hydrocarbon depths (250–2750 m with an increment of 250 m each) and different transmission frequencies (0.125, 0.25, and 0.5 Hz). A two-dimensional (2D) forward GP model was developed for every frequency by utilizing the marine CSEM information. From the results, the uncertainty measurement showed that the estimates were close to the mean. For model validation, the calculated root mean square error (RMSE) and coefficient of variation (CV) proved in good agreement between the computer output and the estimated EM profile at unobserved hydrocarbon depths.

This paper presents a study on processing one dimensional (1D) synthetic seabed logging (SBL) data which were generated through Computer Simulation Technology (CST) software using Gaussian Process Regression (GPR). Seabed Logging (SBL) is an application of electromagnetic (EM) wave emitted from a controlled source to discover hydrocarbon-saturated layers beneath the seabed. In this paper, GPR is proposed as the processing tool to provide any additional information for SBL application. GPR is able to provide predicted mean values and uncertainty measurement in terms of ± standard deviation. The procedures of regressing Gaussian Process (GP) are described thoroughly in this paper. Squared exponential (SE) is chosen as the covariance function used in the GPR. SE covariance function is capable of producing smooth and infinitely differentiable of predicted functional estimates. Log-marginal likelihood is then optimized in order to infer the hyper-parameters involved in the SE covariance function. For model validation, mean square error (MSE) is calculated and observed to determine the reliability of the GPR model in the synthetic SBL data. This shall give an indication that GPR is an appropriate tool for processing nonlinear SBL data with uncertainty quantification and low MSE.