Introduction
The food production sector is a significant energy consumer, representing 15.7% of US energy usage. As the world grapples with climate change, industries, including food production, are making strides towards carbon neutrality. Within this context, the manufacturing of soft-jelled candies emerges as a particularly energy-intensive process. This article delves into the intricacies of candy manufacturing and how mechanical equipment can be optimized for energy efficiency.
The Traditional Candy Making Process
Soft-jelled candies, enjoyed by many around the world, have a rich history of production through a method known as confectionery stoving. This involves:
- Creating a liquid slurry and filling it into candy molds.
- Inserting these molds into stoves or ovens.
- Drying the slurry to achieve the desired candy consistency.
- Cooling the candy to room temperature.
Notably, the drying phase is the most energy-consuming, accounting for up to 84% of the total energy used in a confectionery facility.
Climate’s Impact on Energy Consumption
The energy required for drying is significantly influenced by the facility’s local climate. It’s hypothesized that facilities in dry-hot climates, like Las Vegas, would consume less energy and produce fewer emissions than those in cooler, humid climates, such as Houston.
Innovations in Energy Modeling
To address these challenges, this article introduces a novel quasi-static energy modeling framework. This model simulates transient conditions in a confectionery stove, considering factors like stove temperature, relative humidity, moisture diffusion rate of the candy, and heat transfer rate over time. By integrating real-time meteorological data, the model can accurately capture the effects of local climate on energy consumption.
Methodology
The methodology adopted in this study is rooted in a holistic approach that integrates both the intricacies of the candy-making process and the dynamic nature of environmental factors.
1. Recipe-Driven Modeling: The primary driver for modeling time-dependent food process systems is the specific recipe being used. A comprehensive database was developed, capturing the thermal properties and mass of the ingredients, operation setpoints, and the duration and type of each cycle. This data serves as a foundation for the energy model, dictating the heat and moisture removal necessary to maintain the stove’s conditions as per the recipe.
2. Mathematical Definition of Stove Components: Each component of the stove, from the air handler heating coil to the desiccant wheels, was defined mathematically. While the heating coil was simulated using a basic first law energy balance, the desiccant wheels, which are more complex due to transient mass transfer, were modeled using Becalli’s “simplified model”. This model, comprising eleven equations, predicts the performance of the dehumidifier under varying operating conditions.
3. Integration of Weather Data: Given that the stove requires a consistent percentage of outdoor air to maintain humidity levels, the local climate plays a pivotal role in its energy consumption. To account for this, standardized weather files were used to evaluate stoves in different climate zones. The energy model developed in this study specifically uses actual meteorological year (AMY) weather files as its primary data source.
4. Computational Flow: The high-level computation flow was designed to be iterative and dynamic. The model reads input parameters from both the weather file and the recipe. It then determines if heating is required and calculates the necessary heat input for both the dehumidifier and the air handler. This process is reiterated every 15 minutes over a year, ensuring a comprehensive and detailed analysis.
5. Multi-Objective Optimization (MOO): Given the myriad of variables at play, from local climate to recipe specifics, determining the optimal conditions for candy stoving is a complex task. To navigate this challenge, the study employed the NSGA-II algorithm, a multi-objective optimization tool. This algorithm was chosen for its speed, ease of implementation, and proven track record in similar applications. Through MOO, the study aimed to find the perfect balance between air handler energy, dehumidifier energy, and the initial cost of candy stoves.
By integrating these diverse methodologies, the study offers a comprehensive and nuanced understanding of the energy dynamics at play in candy manufacturing. The specific algorithm used is free available via this link https://platypus.readthedocs.io/en/latest.
The Path to Energy Efficiency
Harnessing the power of Multi-objective Optimization (MOO), the study seeks the most efficient ventilation rate and outdoor air percentage to minimize energy consumption. The MOO algorithm used, known as NSGA-II, identifies a set of optimal solutions that strike a balance between energy consumption and equipment costs. This optimization is tested across various climate zones, from the hot and humid conditions of Zone 2A to the cool and dry environment of Zone 5B.
Key Findings
The research provides several enlightening insights:
This research presents a groundbreaking quasi-static energy model for the confectionery stoving process. By integrating local weather data and optimizing mechanical equipment performance, it offers a roadmap for forecasting energy consumption in specific climates. The findings underscore the benefits of strategic site selection for confectionery stoves and highlight the potential for significant energy and cost savings in the industry.
Furthermore, for those interested in a more in-depth exploration of the methodologies, findings, and implications, a pre-print of the full article is available. This extended version delves deeper into the nuances of the study, providing a comprehensive understanding of the research’s intricacies and its broader implications for the industry.
In the broader context, this study serves as a testament to the importance of integrating advanced modeling and real-world data in the quest for energy efficiency across industries. As industries worldwide grapple with the challenges of sustainability and efficiency, research like this paves the way for informed, data-driven decision-making.