Automation of Marginal Grass Harvesting: Operational, Economic, and Environmental Analysis

Introduction

The increasing demand for sustainable fuels leads to the research of alternative technologies, such as biogas production from lignocellulosic materials. These materials are the most common worldwide and are mainly located in forests or marginal area. However, operational problems have emerged in terms of exploiting them in a cost and environmental efficient way (Christian, 2000; Sørensen and Bochtis, 2010; Pavlou et al., 2016). Difficult terrain topography, difficult access infrastructures and poor soil conditions require different approaches. In particular, efforts are being made to develop smaller, lighter and manoeuvrable machines which are able to perform harvesting operations autonomously (Sørensen and Nielsen, 2005; Sørensen and Bochtis, 2010; Orfanou et al., 2013).

This chapter analyses a marginal grass supply chain in Denmark using alternative harvesting equipment: an automated robot (GrassBot) versus a regular tractor (conventional alternative). In addition, key grass materials accepted by biogas plants (chopped, unchopped and baled grass) are evaluated and compared regarding operational, economical, and environmental performances.

The GrassBot is designed to operate with minimum human intervention but in an efficient and safe way. From the performance point of view, a conventional tractor weighting 2 tons has an output power of around 30kW, the present GrassBot model offers 74 kW with the same weight allowing to process more biomass. This machine is able to cover all the field areas autonomously. To do that, an optimized field path generated by a heuristic algorithm that considers the field boundaries, obstacles, working width, headland passes and driving direction, creates a set of coordinates which represent the parallel fieldwork tracks for traversing the fieldwork area (Sørensen and Nielsen, 2005; Bochtis and Sørensen, 2009). In addition, it is also possible to control the GrassBot using a remote control.

To accomplish the comparison of the harvesting equipment, and also taking into account the supply-chain implications, a process-based model called IRIS is proposed and integrated with a process requirements model (DRIFT) previously developed by Aarhus University (Sørensen and Nielsen, 2005). DRIFT is a program for the modelling and estimating process requirements, namely labor, equipment and time required for each process and also field capacity and efficiency depending on field characteristics and process parameters. The proposed IRIS model receives inputs from the DRIFT model and calculate the resources requirements for a specific production scenario depending on field dimension, harvesting and collection processes. IRIS include materials, energy consumption, labor and equipment required from the equipment manufacturing to the biogas plant gate, where the biomass product is delivered. The parametric characteristics of the IRIS model allow for varying the input parameters such as type of equipment, type of collection or fieldwork conditions. This enables to see how the system is affected by the operational parameters and therefore find the best compromise for each scenario considered. In a second step, IRIS calculate the cost for each supply chain alternative as well as the energy consumed and the green-house gas (GHG) emissions. Through the use of these models, the total supply chain economic and environmental performances of the use of the autonomous robot are compared with conventional harvesting methods for several scenarios of marginal grassland dimensions, as well as for different biomass products, namely unchopped, chopped and bailed biomass. In addition, the transport of the biomass is also considered for the overall economic and environmental analysis, since different biomass density causes different transport requirements.