Hybrid Ventilation System and Soft-Sensors for Maintaining Indoor Air Quality and Thermal Comfort in Buildings

Maintaining both indoor air quality (IAQ) and thermal comfort in buildings along with optimized energy consumption is a challenging problem. This investigation presents a novel design for hybrid ventilation system enabled by predictive control and soft-sensors to achieve both IAQ and thermal comfort by combining predictive control with demand controlled ventilation (DCV). First, we show that the problem of maintaining IAQ, thermal comfort and optimal energy is a multi-objective optimization problem with competing objectives, and a predictive control approach is required to smartly control the system. This leads to many implementation challenges which are addressed by designing a hybrid ventilation scheme supported by predictive control and soft-sensors. The main idea of the hybrid ventilation system is to achieve thermal comfort by varying the ON/OFF times of the air conditioners to maintain the temperature within user-defined bands using a predictive control and IAQ is maintained using Healthbox 3.0, a DCV device. Furthermore, this study also designs soft-sensors by combining the Internet of Things (IoT)-based sensors with deep-learning tools. The hardware realization of the control and IoT prototype is also discussed. The proposed novel hybrid ventilation system and the soft-sensors are demonstrated in a real research laboratory, i.e., Center for Research in Automatic Control Engineering (C-RACE) located at Kalasalingam University, India. Our results show the perceived benefits of hybrid ventilation, predictive control, and soft-sensors

Effective sourcing strategies for perishable product supply chains

Purpose – The purpose of this paper is to assess whether an existing sourcing strategy can effectively supply products of appropriate quality with acceptable levels of product waste if applied to an international perishable product supply chain. The authors also analyse whether the effectiveness of this sourcing strategy can be improved by including costs for expected shelf life losses while generating order policies. Design/methodology/approach – The performance of sourcing strategies is examined in a prototype international strawberry supply chain. Appropriate order policies were determined using parameters both with and without costs for expected shelf life losses. Shelf life losses during transport and storage were predicted using microbiological growth models. The performance of the resulting policies was assessed using a hybrid discrete event chain simulation model that includes continuous quality decay. Findings – The study's findings reveal that the order policies obtained with standard cost parameters result in poor product quality and large amounts of product waste. Also, including costs for expected shelf life losses in sourcing strategies significantly reduces product waste and improves product quality, although transportation costs rise. Practical implications – The study shows that in perishable product supply chain design a trade-off should be made between transportation costs, shortage costs, inventory costs, product waste, and expected shelf life losses. Originality/value – By presenting a generically applicable methodology for perishable product supply chain design, the authors contribute to research and practice efforts to reduce food waste. Furthermore, product quality information is included in supply chain network design, a research area that is still in its infancy

Designing sustainable cold chains for long-range food distribution: Energy-effective corridors on the Silk Road Belt

Modern food production-distribution processes represent a critical stressor for the environment and for natural ecosystems. The rising flows of food across growing and consumption areas couple with the higher expectations of consumers for the quality of products and compel the intensive use of refrigerated rooms and transport means throughout the food supply chain. In order to aid the design of sustainable cold chains that incorporate such aspects, this paper proposes a mixed integer linear programming model to minimize the total energy consumption associated with the cold operations experienced by perishable products. This model is intended for food traders, logistics practitioners, retail managers, and importers collaboratively called to design and plan a cost and environmentally effective supply strategy, physical channels, and infrastructures for cold chains. The proposed model is validated with a case study inspired by the distribution of two example food products, namely fresh apples and ice cream, along the New Silk Road connecting Europe and China. The illustrated analysis investigates the effect of alternative routes and transport modes on the sustainability of the cold chain. It is found that the most energy-efficient route for ice cream is via rail over a northern route and, for apples, is via a southern maritime route, and, for these two routes, the ratios of the total energy consumed to the energy content of the food are 760 and 913, respectively. By incorporating the energy lost due to the food quality decay, the model identifies the optimal route to adopt in accordance with the shelf life and the conservation temperature of each product

Dynamic Modeling and Simulation of SAG Mill Circuits with Pebble Crushing

Grinding is one of the most energy-consuming processes in the mining industry. As a critical part of the comminution process, autogenous grinding (AG) or semi-autogenous grinding (SAG) mills are often used for primary grinding. However, the breakage mechanism of an AG/SAG mill is inefficient in grinding particles of a certain size, typically in the range of 25-55 mm, i.e., pebbles. Therefore, cone crushers are often used as pebble crushers and integrated into AG/SAG mill circuits to break the critical size particles that accumulate in the mill and to increase the performance of the primary grinding circuits.Many studies have been carried out, mainly focusing on optimizing of SAG mills and cone crushers, respectively, but only a few have investigated the dynamic interactions between a SAG mill and its pebble crushers. The scope of this thesis is to examine the dynamic relations between the SAG mill and the pebble crusher in a closed circuit and thus to optimize the circuit efficiency by controlling the pebble crusher operational settings.In this thesis, two modeling techniques are proposed for simulating the dynamics in the grinding process. The first method is the fundamental modeling method, where the underlying physics of the comminution process has been considered. The proposed mill model is divided into sub-processes that include breakage behavior in each sub-division, particle transportation within the mill chamber, and the discharge rate from the mill. The dynamic cone crusher model describes the crusher chamber as a surge bin and predicts the product particle sizes based on crusher CSS and eccentric speed. In the simulation model, other production units such as screens and conveyors are included to describe the dynamics of the circuit better. The flexibility of this method allows one to apply this simulation library to a variety of plants with different configurations.The second modeling technique presented in this study is based on data-driven methods, where two SAG mill power models are developed. The first model calculates the mill power draw by combining several individual data-driven algorithms. The second model uses historical data to forecast the mill power draw in advance. These data-driven methods can make high accuracy predictions based on a specific plant dataset, and find complex nonlinear relations between input variables and target outputs.The results from both simulations and industrial data analysis show that significant dynamic impact can be induced by altering the pebble crusher operational settings. Therefore, the performance (throughput or specific energy) of an AG/SAG closed circuit can be improved with the optimized utilization of its recycle pebble crusher. While the present work is based on simulation and analysis of plant data, full-scale tests and further model development are needed as part of a future study

Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

The recent developments of computer and electronic systems have made the use of intelligent systems for the automation of agricultural industries. In this study, the temperature variation of the mushroom growing room was modeled by multi-layered perceptron and radial basis function networks based on independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP was in the second repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden layer for radial basis function network. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural network with radial basis function was selected as a predictor of the behavior of the system for the temperature of mushroom growing halls controlling system

Integration of different models in the design of chemical processes: Application to the design of a power plant

With advances in the synthesis and design of chemical processes there is an increasing need for more complex mathematical models with which to screen the alternatives that constitute accurate and reliable process models. Despite the wide availability of sophisticated tools for simulation, optimization and synthesis of chemical processes, the user is frequently interested in using the ‘best available model’. However, in practice, these models are usually little more than a black box with a rigid input–output structure. In this paper we propose to tackle all these models using generalized disjunctive programming to capture the numerical characteristics of each model (in equation form, modular, noisy, etc.) and to deal with each of them according to their individual characteristics. The result is a hybrid modular–equation based approach that allows synthesizing complex processes using different models in a robust and reliable way. The capabilities of the proposed approach are discussed with a case study: the design of a utility system power plant that has been decomposed into its constitutive elements, each treated differently numerically. And finally, numerical results and conclusions are presented.Spanish Ministry of Science and Innovation (CTQ2012-37039-C02-02)