Conference Agenda

Empowering communities through the application of established research-based technologies is an effective strategy for advancing the circular economy. A strong example of this approach is the Automated Multi-Commodity Heat Pump Dryer (AMCHPD), a completed research project funded from 2018 to 2020 by the Philippine government through the Department of Science and Technology–Philippine Council for Industry, Energy, and Emerging Technology Research and Development (DOST–PCIEERD). The AMCHPD integrates innovative features that support sustainable agricultural processing. It hygienically dries commodities without chlorine content, operates with zero ozone depletion potential and a modest global warming potential, and enables real-time monitoring of drying conditions and electrical parameters. The technology provides 100 percent chemical-free preservation through a simple cut and dry process while retaining nutritional value and natural product color. Being entirely Filipino-made, it reduces production and maintenance costs while strengthening national technological capability.

To date, the AMCHPD has been adopted in 17 sites nationwide, including 15 in Luzon and one each in the Visayas and Mindanao. The project implemented capacity-building activities with universities, the Department of Trade and Industry, DOST, and technology adopters, focusing on equipment operation, market-driven product development, and livelihood generation using locally available agricultural commodities for sustainable community-based food and non-food processing.

Abstract

Objectives: 1) Quantify optimal temperature and airflow thresholds, as well as the time required to dry wet canola (14% MC) at <0 ^oC and 2) establish safe operating ranges that preserve seed quality in terms of viability.

Methods: Feasibility of atmospheric freeze drying (AFD) of wet canola bulk was evaluated in canola columns at 1.5 m canola depth. The wet canola (14% moisture content, wet basis) was dried at 10, -5, -10, and -15 °C using different superficial air velocities (0.01, 0.08, 0.1, and 0.55 m s^-1). The drying process and seed viability were measured by monitoring the RH and temperature during drying, measuring grain moisture content and germination at the end of the drying tests, and comparing it with natural-air drying.

Results: At 0.08 m s^-1 superficial air velocity ( 54 L s^-1 m^-3), the 14% canola was dried to < 11% moisture content within 8-18 d with no significant germination loss. A higher drying temperature or higher airflow rate resulted in shorter drying times at <0 conditions. For the drying at 10℃, the superficial air velocity should be > 0.01 m s^-1 (>6.9 L s^-1 m^-3 airflow rate) to avoid canola spoilage before drying was completed.

Yellow cassava samples were sliced with average weight, thickness and the diameter of each slice was 5g, 3mm and 30mm respectively. Fermented 24 hours, dewatered and subjected to Infrared, Vacuum and Freeze drying methods. Ten thin layer theoretical and empirical models widely used in describing the drying behavior of agricultural products were fitted to the experimental data in order to select the appropriate model for predicting the drying kinetics of Cassava. The proximate composition and functional properties were determined using standard methods. The Page and parabolic models showed the best fit under certain drying conditions based on the coefficient of determination, Chi-Square (X²⁾ and sum of squares error (SSE). The page model showed an excellent fit with the experimental data while the parabolic model showed an excellent fit with the experimental data. Thus, page and parabolic model can adequately predict the drying kinetics of cassava under drying conditions. Moisture content of cassava were analyzed to range from 46.47-53.43%, ash content of 0.54-1.91%, crude fibre content of 0.33-2.80%, crude protein content of 0.3-2.89%, fat content of 0.23-0.50% and carbohydrate content of 26.99-35.55%. There was no significant difference (P<0.05) in the functional properties when compared with the fresh sample.

Break bulk vessels remain an important, yet under-researched, transport mode for South African citrus exports. Consignments require strict in-transit cold treatments, maintaining fruit temperatures at or below -0.55 °C. Because these temperatures risk chilling injury, understanding thermal heterogeneity within vessel decks and how carton designs (A15C, E15D, and E10D) influence cooling efficacy is critical. A 2D, steady-state computational fluid dynamics (CFD) model of an SRV deck was developed to predict airflow and cooling conditions. Pallets were modelled as a porous media domain using experimental direction-specific momentum source terms to represent carton airflow resistance. Simulations predicted fruit temperatures from -1.5 °C to -1.1 °C. The model demonstrated that primary temperature gradients are linked to deck geometry and pallet positioning relative to air ducts. Conversely, varying packaging types caused a maximum temperature difference of just 0.1 °C, the limit of probe resolution. Simulated thermal behaviour is supported by experimental validations and shipping records, which show no temperature-control deviations linked to packaging. Ultimately, cooling uniformity is excellent and governed by stowage geometry and refrigeration layout, not carton design.

South African citrus cold stores process large volumes of fresh produce under severe strain from rising costs, struggling infrastructure, and stringent phytosanitary requirements. With export volumes projected to surge over the next five years, facilities must urgently rethink strategies to improve efficiency. To address this, a hybrid simulation model was developed in AnyLogic to rapidly and cost-effectively evaluate potential improvements. The model integrates discrete-event simulation to capture pallet movement logistics with agent-based modelling to represent interactions between storage rooms and pallets, ensuring scalability. The model also incorporates a submodel to predict pallet temperature profiles. Validation was achieved by mirroring the layout and operations of an actual cold store. Simulation outputs closely matched the cold stores operational data and yielding valuable insights into operational performance and low-temperature fruit exposure. This validated model will now function as a robust decision-support tool for testing operational strategies and novel facility designs. By simulating "what-if" scenarios, the model aims to drive smoother operations, faster turnaround times, cost savings, and enhanced fruit quality preservation.

Surface condensation on apples are critical phase-change phenomena during cold storage. Current technologies provide limited insight into microclimatic variations within apples in commercial storage. This lack of spatial information constrains storage management to largely static and reactive operation, contributing to challenges such as non-uniform airflow and cooling distribution. To address this gap, a non-intrusive, standalone physical twin was developed to thermally mimic a cluster of 4 apples inside a 300kg bin under commercial storage conditions. This wireless device is equipped with sensors for monitoring key environmental and thermal parameters, including fruit surface temperature and condensation. Coupled with real-time data acquisition, the system enables simulation and forecasting. Results demonstrated close agreement between the thermal responses of the apple bulk and the physical twin. The deviation between the surface temperatures of the physical twin and real apples was less than the typical temperature variation found in the same bin. The integrated condensation sensor reliably detected water formation and retention on the physical twin with close representation of apple behavior, enabling identification of condensation and evaporation phases. This physical twin represents a novel monitoring concept with strong potential for application in the agri-food sector, bridging advanced sensing technologies with climate-smart storage management systems.