Monthly data about oil production at several drilling wells is an example of
spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal
model, i.e. Feedforward Neural Network - VectorAutoregressive (FFNN-VAR) and FFNN
- Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast
accuracy to linearspatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal
models are proposed and applied for forecasting monthly oil production data at three
drilling wells in East Java, Indonesia. There are 60 observations that be divided to two
parts, i.e. the first 50 observations for training data and the last 10 observations for
testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear
spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11)
as linear spatio-temporal models. Moreover, further research about nonlinear spatiotemporal
models based on neural networks and GSTAR is needed for developing new
hybrid models that could improve the forecast accuracy.
In this work, the mixed multivariate T2 control chart's detailed performance evaluation based on PCA mix is explored. The control limit of the proposed control chart is calculated using the kernel density approach. Through simulation studies, the proposed chart's performance is assessed in terms of its capacity to identify outliers and process shifts. When 30% more outliers are included in the data, the proposed chart provides a consistent accuracy rate for identifying mixed outliers. For the balanced percentage of attribute qualities, misdetection happens because of the high false alarm rate. For unbalanced attribute qualities and excessive proportions, the masking effect is the key issue. The proposed chart shows the improved performance for the shift in identifying the shift in the process.
The emergence and continual accumulation of industrial micropollutants such as dyes, heavy metals, organic matters, and pharmaceutical active compounds (PhACs) in the ecosystem pose an alarming hazard to human health and the general wellbeing of global flora and fauna. To offer eco-friendly solutions, living and non-living algae have lately been identified and broadly practiced as promising agents in the bioremediation of micropollutants. The approach is promoted by recent findings seeing better removal performance, higher efficiency, surface area, and binding affinity of algae in various remediation events compared to bacteria and fungi. To give a proper and significant insight into this technology, this paper comprehensively reviews its current applications, removal mechanisms, comparative efficacies, as well as future outlooks and recommendations. In conducting the review, the secondary data of micropollutants removal have been gathered from numerous sources, from which their removal performances are analyzed and presented in terms of strengths, weaknesses, opportunities, and threats (SWOT), to specifically examine their suitability for selected micropollutants remediation. Based on kinetic, isotherm, thermodynamic, and SWOT analysis, non-living algae are generally more suitable for dyes and heavy metals removal, meanwhile living algae are appropriate for removal of organic matters and PhACs. Moreover, parametric effects on micropollutants removal are evaluated, highlighting that pH is critical for biodegradation activity. For selective pollutants, living and non-living algae show recommendable prospects as agents for the efficient cleaning of industrial wastewaters while awaiting further supporting discoveries in encouraging technology assurance and extensive applications.