scholarly journals A Digital Twin Architecture to Optimize Productivity within Controlled Environment Agriculture

2021 ◽  
Vol 11 (19) ◽  
pp. 8875
Author(s):  
Jesus David Chaux ◽  
David Sanchez-Londono ◽  
Giacomo Barbieri

To ensure food security, agricultural production systems should innovate in the direction of increasing production while reducing utilized resources. Due to the higher level of automation with respect to traditional agricultural systems, Controlled Environment Agriculture (CEA) applications generally achieve better yields and quality crops at the expenses of higher energy consumption. In this context, Digital Twin (DT) may constitute a fundamental tool to reach the optimization of the productivity, intended as the ratio between production and resource consumption. For this reason, a DT Architecture for CEA systems is introduced within this work and applied to a case study for its validation. The proposed architecture is potentially able to optimize productivity since it utilizes simulation software that enables the optimization of: (i) Climate control strategies related to the control of the crop microclimate; (ii) treatments related to crop management. Due to the importance of food security in the worldwide landscape, the authors hope that this work may impulse the investigation of strategies for improving the productivity of CEA systems.

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Federica Naspi ◽  
Marco Arnesano ◽  
Francesca Stazi ◽  
Marco D’Orazio ◽  
Gian Marco Revel

Measuring and identifying human behaviours are key aspects to support the simulation processes that have a significant role in buildings’ (and cities’) design and management. In fact, layout assessments and control strategies are deeply influenced by the prediction of building performance. However, the missing inclusion of the human component within the building-related processes leads to large discrepancies between actual and simulated outcomes. This paper presents a methodology for measuring specific human behaviours in buildings and developing human-in-the-loop design applied to retrofit and renovation interventions. The framework concerns the detailed building monitoring and the development of stochastic and data-driven behavioural models and their coupling within energy simulation software using a cosimulation approach. The methodology has been applied to a real case study to illustrate its applicability. A one-year monitoring has been carried out through a dedicated sensor network for the data recording and to identify the triggers of users’ actions. Then, two stochastic behavioural models (i.e., one for predicting light switching and one for window opening) have been developed (using the measured data) and coupled within the IESVE simulation software. A simplified energy model of the case study has been created to test the behavioural approach. The outcomes highlight that the behavioural approach provides more accurate results than a standard one when compared to real profiles. The adoption of behavioural profiles leads to a reduction of the discrepancy with respect to real profiles up to 58% and 26% when simulating light switching and ventilation, respectively, in comparison to standard profiles. Using data-driven techniques to include the human component in the simulation processes would lead to better predictions both in terms of energy use and occupants’ comfort sensations. These aspects can be also included in building control processes (e.g., building management systems) to enhance the environmental and system management.


2021 ◽  
Vol 4 (S2) ◽  
Author(s):  
Anders Clausen ◽  
Krzysztof Arendt ◽  
Aslak Johansen ◽  
Fisayo Caleb Sangogboye ◽  
Mikkel Baun Kjærgaard ◽  
...  

AbstractModel Predictive Control (MPC) can be used in the context of building automation to improve energy efficiency and occupant comfort.Ideally, the MPC algorithm should consider current- and planned usage of the controlled environment along with initial state and weather forecast to plan for optimal comfort and energy efficiency.This implies the need for an MPC application which 1) considers multiple objectives, 2) can draw on multiple data sources, and 3) provides an approach to effectively integrate against heterogeneous building automation systems to make the approach reusable across different installations.To this end, this paper presents a design and implementation of a framework for digital twins for buildings in which the controlled environments are represented as digital entities. In this framework, digital twins constitute parametrized models which are integrated into a generic control algorithm that uses data on weather forecasts, current- and planned occupancy as well as the current state of the controlled environment to perform MPC. This data is accessed through a generic data layer to enable uniform data access. This enables the framework to switch seamlessly between simulation and real-life applications and reduces the barrier towards reusing the application in a different control environment.We demonstrate an application of the digital twin framework on a case study at the University of Southern Denmark where a digital twin has been used to control heating and ventilation.From the case study, we observe that we can switch from rule-based control to model predictive control with no immediate adverse effects towards comfort or energy consumption. We also identify the potential for an increase in energy efficiency, as well as introduce the possibility of planning energy consumption against local electricity production or market conditions, while maintaining occupant comfort.


2016 ◽  
Vol 50 (2) ◽  
pp. 101-113 ◽  
Author(s):  
Masahisa ISHII ◽  
Sadanori SASE ◽  
Hideki MORIYAMA ◽  
Limi OKUSHIMA ◽  
Atsuo IKEGUCHI ◽  
...  

2021 ◽  
Vol 13 (5) ◽  
pp. 2471
Author(s):  
Ajwal Dsouza ◽  
Gordon W. Price ◽  
Mike Dixon ◽  
Thomas Graham

Controlled environment agriculture (CEA), specifically advanced greenhouses, plant factories, and vertical farms, has a significant role to play in the urban agri-food landscape through provision of fresh and nutritious food for urban populations. With the push towards improving sustainability of these systems, a circular or closed-loop approach for managing resources is desirable. These crop production systems generate biowaste in the form of crop and growing substrate residues, the disposal of which not only impacts the immediate environment, but also represents a loss of valuable resources. Closing the resource loop through composting of crop residues and urban biowaste is presented. Composting allows for the recovery of carbon dioxide and plant nutrients that can be reused as inputs for crop production, while also providing a mechanism for managing and valorizing biowastes. A conceptual framework for integrating carbon dioxide and nutrient recovery through composting in a CEA system is described along with potential environmental benefits over conventional inputs. Challenges involved in the recovery and reuse of each component, as well as possible solutions, are discussed. Supplementary technologies such as biofiltration, bioponics, ozonation, and electrochemical oxidation are presented as means to overcome some operational challenges. Gaps in research are identified and future research directions are proposed.


2021 ◽  
Vol 17 (4) ◽  
Author(s):  
Graziella Bedenik ◽  
José Gilmar Nunes De Carvalho Filho ◽  
Elyson Ádan Nunes Carvalho

Bibliographic reviews can be limited when researchers face more specific and new challenges. One rising way to solve this is the systematic review. When performing one, there is a well-defined search, treatment, analysis, and display methodology, which follows the scientific method. In this paper, we propose a systematic review methodology for the electrical engineering field. This kind of methodology allows more objective, concrete, and useful results; bias reduction; and easy reproducibility. For clarification purposes, we provide a case study on sensors, transducers, and actuators for controlled environment agriculture, in which the methodology is applied.


1989 ◽  
Vol 19 (3) ◽  
pp. 469-480 ◽  
Author(s):  
Jeffrey V. Johnson

The topic of this article is the mechanisms by which groups of workers either formally or informally participate in shaping the nature of their work experience. It is proposed that control over the work process is strongly influenced by the character of workplace social groups. This influence process, collective control, determines the possibilities for collective coping with the chronic demands and pressures of various production systems. Collective control is an active strategy, encompassing aspects of social support and social solidarity. It has its greatest health-related effect in occupational situations where the capacity for individual goal attainment is restricted. Empirical data and case study material are presented to illustrate the mechanisms and health-related effects of collectivity.


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