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The production of glycerol derivatives by the esterification process is subject to many constraints related to the yield of the production target and the lack of process efficiency. An accurate monitoring and controlling of the process can improve production yield and efficiency. The purpose of this paper is to propose a real-time optimization (RTO) using gradient adaptive selection and classification from infrared sensor measurement to cover various disturbances and uncertainties in the reactor.

The integration of the esterification process optimization using self-optimization (SO) was developed with classification process was combined with necessary condition optimum (NCO) as gradient adaptive selection, supported with laboratory scaled medium wavelength infrared (mid-IR) sensors, and measured the proposed optimization system indicator in the batch process. Business Process Modeling and Notation (BPMN 2.0) was built to describe the tasks of SO workflow in collaboration with NCO as an abstraction for the conceptual phase. Next, Stateflow modeling was deployed to simulate the three states of gradient-based adaptive control combined with support vector machine (SVM) classification and Arduino microcontroller for implementation.

This new method shows that the real-time optimization responsiveness of control increased product yield up to 13 percent, lower error measurement with percentage error 1.11 percent, reduced the process duration up to 22 minutes, with an effective range of stirrer rotation set between 300 and 400 rpm and final temperature between 200 and 210°C which was more efficient, as it consumed less energy.

In this research the authors just have an experiment for the esterification process using glycerol, but as a development concept of RTO, it would be possible to apply for another chemical reaction or system.

This research introduces new development of an RTO approach to optimal control and as such marks the starting point for more research of its properties. As the methodology is generic, it can be applied to different optimization problems for a batch system in chemical industries.

The paper presented is original as it presents the first application of adaptive selection based on the gradient value of mid-IR sensor data, applied to the real-time determining control state by classification with the SVM algorithm for esterification process control to increase the efficiency.

Supply chain risks (SCRs) have uncertainty and interdependency characteristics that must be incorporated into the risk assessment stage of the SCR management framework. This study aims to develop SCR networks and determine the major risk drivers that impact the performance of the sago starch agro-industry (SSA).

The risk and performance variables were collected from the relevant literature and expert consultations. The Bayesian network (BN) approach was used to model the uncertain and interdependent SCRs. A hybrid method was used to develop the BN structure through the expert’s knowledge acquisitions and the learning algorithm application. Sensitivity analyses were performed to examine the significant risk driver and their related paths.

The analyses of model indicated several significant risk drivers that could affect the performance of the SSA. These SCR including both operational and disruption risks across sourcing, processing and delivery stage.

The implementation of the methodology was only applied to the Indonesian small-medium size sago starch agro-industry. The generalization of findings is limited to industry characteristics. The modelled system is restricted to inbound, processing and outbound logistics with the risk perspective from the industry point of view.

The results of this study assist the related actors of the sago starch agro-industry in recognizing the major risk drivers and their related paths in impacting the performance measures.

This study proposes the use of a hybrid method in developing SCR networks. This study found the significant risk drivers that impact the performance of the sago starch agro-industry.

This study aims to construct models to classify independent smallholder farmers’ (ISFs) plantation suitability and its competitiveness index. It proposes the models with the objective to accommodate ISFs as the main source of raw material for the palm oil industry. It was predicted that the supply of oil palm fresh fruit bunch would depend on ISFs’ plantations due to the government policy that restricts the expansion of the plantations.

The study was designed using a geographic information system approach and analytical hierarchy process for mapping the suitability of smallholder farmers’ oil palm plantation in the Kampar Regency. The competitiveness index was measured using a modified Diamond Porter framework and competitiveness index quantification. The model was conducted on 177 respondents from five districts in Kampar Regency.

The results indicated that it mapped 128,936.759 hectares area of ISFs’ oil palm plantation in the Kampar Regency. The results of plantation suitability showed that only 13.88% of plantations owned by ISFs were potential, 71.21% of them were in the developing category and 14.91% of plantations were non-potential. The competitiveness index showed there were only 7.91% of them at the developed competitive position, 73.45% at developing a competitive position and 18.64% at the least-developed position.

The paper includes implications for developing ISFs’ capacity building and best practice management for oil palm optimization, resulting in bargaining positions and social well-being.

The study had succeeded to visualize ISFs’ plantation area suitability and competitiveness at Kampar Regency, Riau Province. The model provides a brisk understanding and valuable information about ISFs’ conditions spatially. It offers specific outcomes and becomes important in optimize and develop the existing plantations at the right time and exact location.