Yogendra Shastri

Integrated strategic and tactical biomass–biofuel supply chain optimization

To ensure effective biomass feedstock provision for large-scale biofuel production, an integrated biomass supply chain optimization model was developed to minimize annual biomass-ethanol production costs by optimizing both strategic and tactical planning decisions simultaneously. The mixed integer linear programming model optimizes the activities range from biomass harvesting, packing, in-field transportation, stacking, transportation, preprocessing, and storage, to ethanol production and distribution. The numbers, locations, and capacities of facilities as well as biomass and ethanol distribution patterns are key strategic decisions; while biomass production, delivery, and operating schedules and inventory monitoring are key tactical decisions. The model was implemented to study Miscanthus-ethanol supply chain in Illinois. The base case results showed unit Miscanthus-ethanol production costs were

Optimization of Miscanthus Harvesting and Handling as an Energy Crop: BioFeed Model Application

0.72L(-1) of ethanol. Biorefinery related costs accounts for 62% of the total costs, followed by biomass procurement costs. Sensitivity analysis showed that a 50% reduction in biomass yield would increase unit production costs by 11%.

Agent-based analysis of biomass feedstock production dynamics.

The success of the bioenergy sector depends significantly on ensuring efficient and sustainable biomass feedstock production and provision, which requires a comprehensive systems theory based approach. BioFeed is a system-level model that has been proposed to optimize the feedstock production and provision activities. It has been applied in the past to study switchgrass production in Illinois. This work presents recent additions to the BioFeed model to enable a more accurate representation of various biomass production activities for energy crops. While maintaining the original model framework that focuses on farm-level design and operational issues in addition to storage and transportation logistics, new biomass packing and size reduction operations such as pelletization and grinding have been added. The selection and operation of biomass handling equipment such as loaders, unloaders, and in-field transportation equipment have also been incorporated. The addition of these new operations created the challenge of ensuring the logical validity of the operational sequence during model simulation. A superstructure of all possible operational sequences was developed, and the biomass form at the output of every piece of equipment was tracked to ensure appropriate equipment selection. The model was then applied to a case study of Miscanthus production as the energy crop in southern Illinois. The results showed that the optimized delivered cost based on existing technology was about

Biomass feedstock preprocessing and long-distance transportation logistics

45 Mg-1. Biomass packing and storage were important components of the total cost distribution. The potential alternatives to reduce the delivered cost included using a single-pass mowing and baling operation, increasing the packing throughput capacity, and extending the harvesting window.

Green process design, green energy, and sustainability: A systems analysis perspective

The success of the bioenergy sector based on lignocellulosic feedstock will require a sustainable and resilient transition from the current agricultural system focused on food crops to one also producing energy crops. The dynamics of this transition are not well understood. It will be driven significantly by the collective participation, behavior, and interaction of various stakeholders such as farmers within the production system. The objective of this work is to study the system dynamics through the development and application of an agent-based model using the theory of complex adaptive systems. Farmers and biorefinery, two key stakeholders in the system, are modeled as independent agents. The decision making of each agent as well as its interaction with other agents is modeled using a set of rules reflecting the economic, social, and personal attributes of the agent. These rules and model parameters are adapted from literature. Regulatory mechanisms such as Biomass Crop Assistance Program are embedded in the decision-making process. The model is then used to simulate the production of Miscanthus as an energy crop in Illinois. Particular focus has been given on understanding the dynamics of Miscanthus adaptation as an agricultural crop and its impact on biorefinery capacity and contractual agreements. Results showed that only 60% of the maximum regional production capacity could be reached, and it took up to 15 years to establish that capacity. A 25% reduction in the land opportunity cost led to a 63% increase in the steady- state productivity. Sensitivity analysis showed that higher initial conversion of land by farmers to grow energy crop led to faster growth in regional productivity.

An efficient algorithm for large scale stochastic nonlinear programming problems

Biomass‐based biofuels have gained attention because they are renewable energy sources that could facilitate energy independence and improve rural economic development. As biomass supply and biofuel demand areas are generally not geographically contiguous, the design of an efficient and effective biomass supply chain from biomass provision to biofuel distribution is critical to facilitate large‐scale biofuel development. This study compared the costs of supplying biomass using three alternative biomass preprocessing and densification technologies (pelletizing, briquetting, and grinding) and two alternative transportation modes (trucking and rail) for the design of a four‐stage biomass–biofuel supply chain in which biomass produced in Illinois is used to meet biofuel demands in either California or Illinois. The BioScope optimization model was applied to evaluate a four‐stage biomass–biofuel supply chain that includes biomass supply, centralized storage and preprocessing (CSP), biorefinery, and ethanol distribution. We examined the cost of 15 scenarios that included a combination of three biomass preprocessing technologies and five supply chain configurations. The findings suggested that the transportation costs for biomass would generally follow the pattern of coal transportation. Converting biomass to ethanol locally and shipping ethanol over long distances is most economical, similar to the existing grain‐based biofuel system. For the Illinois–California supply chain, moving ethanol is

Switchgrass - practical issues in developing a fuel crop.

0.24 gal−1 less costly than moving biomass even in densified form over long distances. The use of biomass pellets leads to lower overall costs of biofuel production for long‐distance transportation but to higher costs if used for short‐distance movement due to its high capital and processing costs. Supported by the supply chain optimization modeling, the cellulosic‐ethanol production and distribution costs of using Illinois feedstock to meet California demand are

Engineering and science of biomass feedstock production and provision

0.08 gal−1 higher than that for meeting local Illinois demand.

System Level Analysis of Biomass Feedstock Production for Bioenergy Sector

This paper presents a systems analysis perspective that extends the traditional process design framework to green process design, green energy and industrial ecology leading to sustainability. For green process design this involves starting the design decisions as early as chemical and material selection stages on one end, and managing and planning decisions at the other end. However, uncertainties and multiple and conflicting objectives are inherent in such a design process. Uncertainties increase further in industrial ecology. The concept of overall sustainability goes beyond industrial ecology and brings in time dependent nature of the ecosystem and multi-disciplinary decision making. Optimal control methods and theories from financial literature can be useful in handling the time dependent uncertainties in this problem. Decision making at various stages starting from green process design, green energy, to industrial ecology, and sustainability is illustrated for the mercury cycling. Power plant sector is a major source of mercury pollution. In order to circumvent the persistent, bioaccumulative effect of mercury, one has to take decisions at various levels of the cycle starting with greener power systems, industrial symbiosis through trading, and controlling the toxic methyl mercury formation in water bodies and accumulation in aquatic biota.

Plant and Control-Relevant Nonlinearity Analysis of a CSTR: A Case Study

The class of stochastic nonlinear programming (SNLP) problems is important in optimization due to the presence of nonlinearity and uncertainty in many applications, including those in the field of process systems engineering. But despite the apparent importance of such problems, the solution algorithms for these problems have found few applications due to the severe computational and structural restrictions. To that effect, this work proposes a new algorithm for a computationally efficient solution of the SNLP problems. Starting with the basic structure of the traditional L-shaped method, the new algorithm, called the L-shaped BONUS, incorporates the reweighting scheme to ease the computational load in the second stage recourse function calculation. The reweighting idea has previously been successfully used in optimization in BONUS, also an algorithm to solve the SNLP problems. The proposed algorithm is analyzed using different case study problems, including a blending problem relevant to the process industry and a large scale, novel sensor placement problem for water security networks. The results for all the problems show considerable savings in the computational time without compromising the accuracy, the performance being better for the Hammersley sequence sampling technique as compared to the Monte Carlo sampling technique.