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Purpose – The purpose of this research is to study the process conditions that give best yield and expected compositions of liquid smoke products that result during the pyrolisis process relying on predetermined variables.

Design/Methodology/Approach – Pyrolisis process running times are varied, that is, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, and 6 hourly. Condensing temperature maintained remained 25–30 °C. Products identification was applied by using gas chromotography mass spectroscopy.

Findings – Based on the research output, it was concluded that process conditions which give maximum yield were achieved when using double unit condenser (DUC) and time optional four hours, and it provides maximum volume liquid smoke product, and compositions of pyrolisis products. The process also created seven components, namely nepthalene, propanoic acid, 3,7 nanodiena, 2 metilguaiakol, 2-metoksi 4-methyl phenol, 4 ethyl-2 metoksil phenol, oxybanzene. Applying DUC during condensation phase may increase condensing force thereafter obtaining resulted products between 200% and 300% rather than using single unit condenser (SUC).

Research Limitations/Implications – This research was conducted on a fixed batch reactor made of a metal plate with a thickness of 3.0 mm. It carries 200 kg in capacity. In this phase, the moisture of candlenut shells might be kept in 10–12.5% wt. Process temperature applied ranged within 350–500 °C.

Originality/Value – In addition the study increased the theorical of understanding about pyrolisis process and Improving the production of liquid smoke from candlenut shell by pyrolisis process using the method of vapor condensation (Double unit condensor).

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.

Purpose – This work aims to study the treatment of adsorbant on the increasing liquid hydrocarbon quality produced by pyrolysis low density polyethylene (LDPE) plastic waste at low temperature. The hydrocarbon distribution, physicochemical properties and emission test were also studied due to its application in internal combustion engine. This research uses pure Calcium carbonate (CaCO₃) and pure activated carbon as adsorbant, LDPE type clear plastic samples with control variable that is solar gas station.

Design/Methodology/Approach – LDPE plastic waste of 10 kg were vaporized in the thermal cracking batch reactor using LPG 12 kg as fuel at range temperature from 100 to 300°C and condensed into liquid hydrocarbon. Furthermore, this product was treated with the mixed CaCO₃ and activated carbon as adsorbants to decrease contaminant material.

Findings – GC-MS identified the presence of carbon chain in the range of C6–C44 with 24.24% of hydrocarbon compounds in the liquid. They are similar to diesel (C6–C14). The 30% of liquid yields were found at operating temperature of 300°C. The calorific value of liquid was 46.021 MJ/Kg. This value was 5.07% higher than diesel as control.

Originality/Value – Hydrocarbon compounds in liquid produced by thermal cracking at a low temperature was similar to liquid from a catalytic process.

Purpose – The purpose of this study is to explore the concept of the economic lot sizing and the time cycle period of reordering. The stochastic demand is quite common in the real environment of a cement retailer. The study compares three methods to obtain the optimal solution of a lot-sizing ordering from the real case of the previous study where the dataset is collected from the area of some retailers at Banda Aceh Province of Indonesia.

Design/Methodology/Approach – The problem model appears when the retailer with shortage has to fulfill the lot size in the optimal condition to the stochastic demand while at the same time has the backlog condition. Moreover, when the backorder needs the time horizon for replenishment where this condition influences the holding cost at the store, many retailers try to solve this problem to minimize the holding cost, but on the other side, it should fulfill the customer demand. Three methods are explored to identify that condition: a Wagner–Whitin algorithm, the Silver–Meal heuristic, and the holding and ordering costs. The three methods are applied to the lot sizing when there is a backlog.

Findings – The results of this study show that the Wagner–Whitin algorithm outperforms the other two methods. It shows that the performance increases around 27% when compared to the two other methods in this study.

Research Limitations/Implications – All models are almost approximate and useful to determine the cycle period on stochastic demand.

Practical Implications – The calculation of the dataset with the three methods would give the simple example to the retailer when he faces the uncertainty demand models. The prediction of the calculation is done accurately than the constant calculation, which is more economic.

Social Implications – The calculation will contribute to much better predictions in many cases of uncertainty.

Originality/Value – This is a initial comparative model among other methods to achieve the optimal stock and order for a retailer