Organisational challenges for local maize value chains in the biobased economy

Societal challenges drive an increased interest to transform our fossil resources based to a biobased economy, in which biomass is used for the production of bioenergy and biomaterials. Research aiming to enhance this biobased economy often focuses on the technical and techno-economic aspects of converting biomass into value-added biobased products, but fails to take into account non-technical aspects, such as the organizational challenges related to local biomass value chains. These organizational challenges originate from the unique characteristics of the biomass itself, and those of the economic agents involved in the value chain. In this dissertation, we therefore focused on the organizational aspects of local biomass value chains for new applications within the biobased economy. We used local maize value chains in Flanders as case-study. Our research integrated findings from qualitative research with simulation results from a quantitative dynamic modelling approach, being agent-based modelling. We demonstrated the importance of the local context in the trade of silage maize, and identified several organizational challenges that need to be addressed for the development of a corn stover value chain in Flanders. This allows us to formulate five practical recommendations for practitioners: (1) try to work with intermediaries when you are a new entrant into an already existing local biomass value chain; (2) retain an adequate level of flexibility; (3) make a well-considered choice about the organizational form of new value chains; (4) make sure all stakeholders are involved when developing new local biomass value chains for new applications in the biobased economy; and (5) pay special attention to create trust and enthusiasm for the new value chain amongst all stakeholders involved. In general, we advocate a value chain perspective when developing new local biomass value chains for the biobased economy

Optimization and cost analysis of lignocellulosic biomass feedstocks supply chains for biorefineries

This study estimated the biomass harvest and transport cost considering single pass biomass harvest with bulk and bale collections of biomass. Lignocellulosic biomass feedstocks costs were estimated using both corn stover and switchgrass as part of the feedstock supply chain. Harvest and transport cost for multi-pass biomass harvest operations using multiple feedstocks were analyzed and the optimal number of machines for all unit operations were estimated for each supply chain. This dissertation calculated and compared the biomass harvest and transport cost for single pass biomass harvest with bulk and bale collections of biomass. The objective of the research was to find the optimal number of machines, and least cost biomass harvest and transportation costs based on the harvest window, machine capacity, farm sizes and yield of the biomass. The least cost model was developed using the mixed integer non-linear programming model developed in General Algebraic Modeling System. The cost of harvest and transport using the bulk stover collection method was estimated about $25 Mg-1 ($23 ton-1) considering a transport distance of 3.2 km (2 miles) for primary storage from the field with the harvestable stover yield of 4.4 Mg ha-1 (2 ton ac-1) for the farm size of 2,000 ha. (5,000 ac.) Biomass feedstocks cost at the gate of biorefinery was estimated for multi-pass harvest systems with multi-feedstocks. Corn stover was considered a by-product of grain production and switchgrass as a single product. Planting and establishment cost was also considered along with harvest and transport cost for switchgrass. The cost of switchgrass varied from $75 Mg-1 to$97 Mg-1 ($68 ton-1 to$88 ton-1) and cost of corn stover varied from $75 Mg-1 to$97 Mg-1 ($20 ton-1 to$25 ton-1) respectively with the farm sizes variation from 400 ha to 2,000 ha (1,000 ac to 5,000 ac)

DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR CAPACITY PLANNING FROM GRAIN HARVEST TO STORAGE

This dissertation investigated issues surrounding grain harvest and transportation logistics. A discrete event simulation model of grain transportation from the field to an on-farm storage facility was developed to evaluate how truck and driver resource constraints impact material flow efficiency, resource utilization, and system throughput. Harvest rate and in-field transportation were represented as a stochastic entity generation process, and service times associated with various material handling steps were represented by a combination of deterministic times and statistical distributions. The model was applied to data collected for three distinct harvest scenarios (18 total days). The observed number of deliveries was within ± 2 standard deviations of the simulation mean for 15 of the 18 input conditions examined, and on a daily basis, the median error between the simulated and observed deliveries was -4.1%. The model was expanded to simulate the whole harvest season and include temporary wet storage capacity and grain drying. Moisture content changes due to field dry down was modeled using weather data and grain equilibrium moisture content relationships and resulted in an RMSE of 0.73 pts. Dryer capacity and performance were accounted for by adjusting the specified dryer performance to the observed level of moisture removal and drying temperature. Dryer capacity was generally underpredicted, and large variations were found in the observed data. The expanded model matched the observed cumulative mass of grain delivered well and estimated the harvest would take one partial day longer than was observed. Usefulness of the model to evaluate both costs and system performance was demonstrated by conducting a sensitivity analysis and examining system changes for a hypothetical operation. A dry year and a slow drying crop had the largest impact on the system’s operating and drying costs (12.7% decrease and 10.8% increase, respectively). The impact of reducing the drying temperature to maintain quality in drying white corn had no impact on the combined drying and operating cost, but harvest took six days longer. The reduced drying capacity at lower temperatures resulted in more field drying which counteracted the reduced drying efficiency and increased field time. The sensitivity analysis demonstrated varied benefits of increased drying and transportation capacity based on how often these systems created a bottleneck in the operation. For some combinations of longer transportation times and higher harvest rates, increasing hauling and drying capacity could shorten the harvest window by a week or more at an increase in costs of less than $12 ha-1. An additional field study was conducted to examine corn harvest losses in Kentucky. Total losses for cooperator combines were found to be between 0.8%-2.4% of total yield (86 to 222 kg ha-1). On average, the combine head accounted for 66% of the measured losses, and the total losses were highly variable, with coefficients of variation ranging from 21.7% to 77.2%. Yield and harvest losses were monitored in a single field as the grain dried from 33.9% to 14.6%. There was no significant difference in the potential yield at any moisture level, and the observed yield and losses displayed little variation for moisture levels from 33.9% to 19.8%, with total losses less than 1% (82 to 130 kg dry matter ha-1). Large amounts of lodging occurred while the grain dried from 19.8% to 14.6%, which resulted in an 18.9% reduction in yield, and harvest losses in excess of 9%. Allowing the grain to field dry generally improved test weight and reduced mechanical damage, however, there was a trend of increased mold and other damage in prolonged field drying

Challenges for Sustainable Biomass Utilisation : Proceedings of the Chilean-German Biociclo Workshop (Karlsruhe, 26.03.2009)

The energetic use of biomass can provide solutions for the growing worldwide demand for energy and fuel. This book contains the contributions for the final workshop of the "Biociclo" research exchange between the Universidad de Concepción and the Universität Karlsruhe. It reflects interdisciplinarity of the workshop\u27s participants with contributed papers about Biomass Utilization Paths in Chile, Pyrolysis and Life-Cycle Assessment of Biomass and Logistic Concepts of Biomass Utilization Concepts

The importance of high crop residue demand on biogas plant site selection, scaling and feedstock allocation – A regional scale concept in a Hungarian study area

In regions characterised by intensive agriculture, livestock manure is a commonly used feedstock for biogas production. Due to its expensive transportation, manure sources are often the sole criteria during biogas plant site selection, regarding feedstock supply. Encouraging biogas plant operators to use larger amounts of crop residues in the feedstock is favourable from an energy management viewpoint, but its spatial projection on resource logistics and its significance on biogas plant selection is less investigated. In this study, scenarios were created with different feedstock compositions considering constant manure and varying crop residue ratios. Based on their potential biogas yields and the location of livestock farms, a manure source-oriented site selection and facility scaling was made in a Hungarian study area. The applied GIS-based feedstock allocation and logistic analysis defined the crop acquisition possibilities and optimal transportation routes, assuming multiple resource-competitive biogas plants. The results indicate that feedstock composition can indirectly impact the site selection procedure and supply security if high crop residue demand is considered. Resource acquisition possibilities and economic feasibility are significantly affected by the location and density of the proposed biogas plants and their relative position to the crop supply areas. Due to the geographical heterogeneity of the supply side and the demand points, the transportation costs of crop residues and the digestate exceed those of the manure in all scenarios, which draws attention to the importance of spatial availability of crop residues during biogas plant site selection and scaling

Strategic analysis and optimization of bioethanol supply chains

In modern times, the interest in renewable energy has been increasing considerably in response to the growing energy demand and to the simultaneous concern about global warming effects. The urgency of this issue is related to dissociation between the perspective of a steady growth in demand for fuel and its supply, which is projected to become ever more uncertain and expensive. The phenomenon of climate change is widely recognized as a consequence of the increased concentration of greenhouse gases (GHG) in the atmosphere caused by anthropogenic activity, and to which the transport sector is a significant contributor. Among biofuels, biomass-based ethanol has been in a leading position for substituting petroleum-based road-fuels. Even if its actual carbon footprint is still debated, it is generally acknowledged a reduction in net GHG emissions with respect to oil. The complexity of the context discussed previously, guides us to the transition towards a more sustainable transport system which requires the adoption of effective quantitative tools able to encompassing the problem to the whole production chain (supply chain), that may help defining a more comprehensive view of biofuels. In dealing with such problems involving high decisional level, the analytical modelling is recognized as the best optimization option, particularly in the initial phase of design of unknown infrastructures in order to cope with a comprehensive management of production systems taking into account all supply chain stages. Mixed Integer Linear Programming (MILP) in particular, emerges as one of the most suitable tools in determining the optimal solutions of complex supply chain design problems where multiple alternatives are to be taken into account. In this sense, the multi-objective MILP (moMILP) enables simultaneous consideration of conflicting criteria (i.e., financial, environmental) to assist the decisions of interested parties on biofuels industry at strategic and tactical levels. Moreover, this complex analysis is addressed effectively by incorporating the principles of Life Cycle Analysis (LCA) within supply chain analysis techniques aiming at a quantitative assessment of the environmental burdens of each supply chain stage. Accordingly, the main purpose of the research presented in this Thesis is to cover this gap of knowledge in the literature. In the context of the development and adoption of bioenergy systems, the overall objective of this work is to provide quantitative and deterministic tools to analyze and optimize the supply chain as whole, to thereby identify the most suitable and feasible strategies for the development of future road transport systems. In this sense, the research design for this Thesis begins with the development and analysis of a multi-period moMILP modelling framework for the design and the optimization of bioethanol supply chain where economics and environmental sustainability (GHG emissions reductions potential) for first generation ethanol is addressed, considering possibilities of several technologies integration (including biogas production). Then, the analysis is focused on the general interactions of market policies under the European Emission Trading System in order to enhance the bioethanol market development trends to boost sustainable production of bioethanol. Next, a comprehensive modelling analysis to predict commodity price evolution dynamics and to extend the price forecasts to other goods related to bioethanol production is addressed. An assessment of the impact on the supply chain design of the recent proposed by the European Commission to amend the existing Directive in terms of accountability technique for biofuels is analyzed and discussed. Besides, multi-criteria decision making tools to support strategic design and planning on biofuel supply chains including several Game Theory features are evaluated. Finally to close up, the main achievements of the Thesis are exposed as well as the main shortfalls and possible future research lines are outlined. Models capabilities in steering decisions on investments for bioenergy systems are evaluated in addressing real world case studies referring to the emerging bioethanol production in Northern Italy

Downscaling of agricultural market impacts under bioeconomy development to the regional and the farm level—An example of Baden‐Wuerttemberg

The expansion of the bioeconomy sector will increase the competition for agricultural land regarding biomass production. Furthermore, the particular path of the expansion of the bioeconomy is associated with great uncertainty due to the early stage of technology development and its dependency on political framework conditions. Economic models are suitable tools to identify trade-offs in agricultural production and address the high uncertainty of the bioeconomy expansion. We present results from the farm model Economic Farm Emission Model of four bioeconomy scenarios in order to evaluate impacts and trade-offs of different potential bioeconomy developments and the corresponding uncertainty at regional and farm level in Baden-Wuerttemberg, Germany. The demand-side effects of the bioeconomy scenarios are based on downscaling European Union level results of a separate model linkage between an agricultural sector and an energy sector model. The general model results show that the expanded use of agricultural land for the bioeconomy sector, especially for the cultivation of perennial biomass crops (PBC), reduces biomass production for established value chains, especially for food and feed. The results also show differences between regions and farm types in Baden-Wuerttemberg. Fertile arable regions and arable farms profit more from the expanded use of biomass in the bioeconomy than farms that focus on cattle farming. Latter farms use the arable land to produce feed for the cattle, whereas arable farms can expand feedstock production for new value chains. Additionally, less intensive production systems like extensive grassland suffer from economic losses, whereas the competition in fertile regions further increases. Hence, if the extensive production systems are to be preserved, appropriate subsidies must be provided. This emphasizes the relevance of downscaling aggregated model results to higher spatial resolution, even as far as to the decision maker (farm), to identify possible contradicting effects of the bioeconomy as well as policy implications.Ministerium für Wissenschaft, Forschung und Kunst Baden‐Württemberg http://dx.doi.org/10.13039/501100003542Peer Reviewe