1,450 research outputs found

    Dynamic simulation driven design and management of production facilities in agricultural/food industry

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    An industrial plant in the agro-food sector can be considered a complex system as it is composed of numerous types of machines and it is characterized by a strong variation (seasonality) in the agricultural production. Whenever the dynamic behavior of the plants during operation is considered, system and design complexities increase. Reliable operation of food processing farms is primarily dependent on perfect balance between variable supply and product storage at each given time. To date, the classical modus operandi of food processing management systems is carried out under stationary and average conditions. Moreover, most of the systems installed for agricultural and food industries are sized using average production data. This often results in a mismatch between the actual operation and the expected operation. Consequently, the system is not optimized for the needs of a specific company. Also, the system is not flexible to the evolution that the production process could possibly have in the future. Promising techniques useful to solve the above-described problems could possibly be borrowed from demand side management (DSM) in smart grid systems. Such techniques allow customers to make dynamically informed decisions regarding their energy demand and help the energy providers in reducing the peak load demand and reshape the load profile. DSM is successfully used to improve the energy management system and we conjecture that DSM could be suitably adapted to food processing management. In this paper we describe how DSM could be exploited in the intelligent management of production facilities serving agricultural and food industry. The main objective is, indeed, to present how methods for modelling and implementing the dynamic simulation used for the optimization of the energy management in smart grid systems can be applied to a fruit and vegetables processing plant through a suitable adaptation

    Green food supply chain design considering risk and post-harvest losses: a case study

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    The global food insecurity, malnourishment and rising world hunger are the major hindrances in accomplishing the zero hunger sustainable development goal by 2030. Due to the continuous increment of wheat production in the past few decades, India received the second rank in the global wheat production after China. However, storage capacity has not been expanded with similar extent. The administrative bodies in India are constructing several capacitated silos in major geographically widespread producing and consuming states to curtail this gap. This paper presents a multi-period single objective mathematical model to support their decision-making process. The model minimizes the silo establishment, transportation, food grain loss, inventory holding, carbon emission, and risk penalty costs. The proposed model is solved using the variant of the particle swarm optimization combined with global, local and near neighbor social structures along with traditional PSO. The solutions obtained through two metaheuristic algorithms are compared with the optimal solutions. The impact of supply, demand and capacity of silos on the model solution is investigated through sensitivity analysis. Finally, some actionable theoretical and managerial implications are discussed after analysing the obtained results

    Chapter 5: Food Security

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    The current food system (production, transport, processing, packaging, storage, retail, consumption, loss and waste) feeds the great majority of world population and supports the livelihoods of over 1 billion people. Since 1961, food supply per capita has increased more than 30%, accompanied by greater use of nitrogen fertilisers (increase of about 800%) and water resources for irrigation (increase of more than 100%). However, an estimated 821 million people are currently undernourished, 151 million children under five are stunted, 613 million women and girls aged 15 to 49 suffer from iron deficiency, and 2 billion adults are overweight or obese. The food system is under pressure from non-climate stressors (e.g., population and income growth, demand for animal-sourced products), and from climate change. These climate and non-climate stresses are impacting the four pillars of food security (availability, access, utilisation, and stability)

    Development of a modified dynamic energy and greenhouse gas reduction planning approach through the case of Indian power sector

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    Energy and Environmental Analysis is a method to evaluate utility of any energy system by finding the requirement of energy and resulting emissions through all the materials and processes used to build and use any system over its entire life and also to demolish it at the end of life. Relationship between the cumulative energy demand and cumulative emissions with energy output from the system establishes indicators for its utility in terms "Energy Yield Ratio" and "Emission Coefficient". Energy and Environmental Planning is a macroscopic exercise used for conducting futuristic studies through dynamic assessment of the defined reference energy system comprising of many alternatives and constraints. It is done to find the optimum solution for certain objective function often system cost minimization through meeting system requirements such as the energy demand. To establish link between these two approaches, a new methodology has been formulated in this work. It has been done through linking the Cumulative Energy Demand (a system specific, energy analysis parameter of static nature), and the overall energy demand which is a dynamic parameter governed by its rate of growth. With the help of this new method, Cumulative Energy Demand of any system acts as a barrier for growth as it takes away energy from the overall energy pool. The value of maximum growth obtained through equilibrium equations has been exogenously supplied to the energy planning tool and thus the link between the two different approaches has been established. This work demonstrates the method for each of the above approaches separately and then jointly, involving various technologies for power generation. A much widely used energy planning software MARKAL (MARket ALlocation), has been used for carrying out planning related analysis which treats the defined Reference Energy System as a dynamic bottom-up problem and finds the objective function through obtaining a partial equilibrium at all intermediate stages. The above mentioned methodology has been validated through the analysis of Indian power sector. There has been an unsatisfactory growth in this sector during past few years which has resulted into increase in the shortage of power supply. Besides, pressure for controlling the emission of greenhouse gases is increasing day by day. Therefore, model of the Indian power sector has been developed and several scenarios have been made to cover various major possibilities for the future. Effects of introduction of CO2 taxes at different rates have also been modeled through the developed approach to find the consequential change in the structure of power sector and to assess the potential for reduction in emissions. Results obtained indicate that during the period up to the year 2025, there exists a possibility of reducing carbon dioxide emissions up to about 25%. The system will incur about 100 to 140 rupees (approximately 2.5 to 3.5 Euro) for reduction of each ton of carbon dioxide depending upon the target and hence decided emission tax rates. These costs are much less as compared to the rates found for other countries like Germany, as the renewable energy based power generation is relatively much cheaper in India. It has also been found that it would be better to pay more attention towards large hydro and wind power as they tend to be more economic in almost all scenarios. There also exists a possibility for natural gas based power plants to replace coal based plants but at present Pressurized Fluidized Bed Combustion based coal power plants would be better. As one of the results it is also inferred that advanced technologies like Integrated Gasification Combined Cycle based coal power plants, oil based power plants and photovoltaic power plants are not competitive enough with their present cost and performance criteria, in any of the considered scenarios

    The implications of the Paris Agreement on carbon dioxide removal (CDR) - techno-economics, potential, efficiency and permanence of CDR pathways

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    Tackling the many unprecedented changes observed in the climate requires immediate and deep mitigation efforts, including carbon dioxide removal (CDR). Despite the many different CDR methods proposed in the literature, it is still unclear how, where, and when carbon dioxide (CO2) removal will be deployed in a sustainable, feasible, and efficient manner. Owing to uncertainties about their CO2 removal and scale-up potentials, concerns about their environmental impacts and their competition for bio-geophysical resources, and their socio-economic challenges, the deployment of CDR at scales that are consistent with the Paris Agreement’s 1.5°C objectives is controversial. This thesis investigates the spatio-temporal deployment of portfolios of archetypal CDR technologies and practices — namely afforestation/reforestation, bioenergy with carbon capture and storage (BECCS), biochar, direct air capture of CO2 with storage (DACCS), and enhanced weathering (EW) —, and seeks to provide insights into their techno-economics, potential, efficiency and permanence. We find that the conditions under which CO2 is removed from the atmosphere in an efficient, timely, and permanent manner vary highly across the different CDR methods. Consequently, their roles and values, when deployed to deliver the Paris Agreement’s 1.5°C objectives, also vary with the region and time of deployment. Additionally, international/inter-regional cooperation is emphasised to deploy CDR most cost-efficiently and equitably — by allowing regions well-endowed with CDR potential to trade CDR surplus with regions for which delivering CDR is more difficult, or more costly. However, such cooperation needs to be implemented as immediately as possible to prevent from greater costs, carried mostly by future generations. Importantly, international/inter-regional markets for negative emissions trading are shown to be of important economic value, not only for beneficiaries of CDR, but also for providers. Finally, we find that time preference — the choice of time horizon for defining permanence — in CDR accounting methods impacts the portfolio of CDR methods deployed, with short-term methods favouring the deployment of temporary CDR methods, more affordable but also more liable to the risks of CO2 re-release/non-permanence, and with long-term methods favouring permanent CDR methods, less risky but more expensive.Open Acces

    A systematic literature review on machine learning applications for sustainable agriculture supply chain performance

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    Agriculture plays an important role in sustaining all human activities. Major challenges such as overpopulation, competition for resources poses a threat to the food security of the planet. In order to tackle the ever-increasing complex problems in agricultural production systems, advancements in smart farming and precision agriculture offers important tools to address agricultural sustainability challenges. Data analytics hold the key to ensure future food security, food safety, and ecological sustainability. Disruptive information and communication technologies such as machine learning, big data analytics, cloud computing, and blockchain can address several problems such as productivity and yield improvement, water conservation, ensuring soil and plant health, and enhance environmental stewardship. The current study presents a systematic review of machine learning (ML) applications in agricultural supply chains (ASCs). Ninety three research papers were reviewed based on the applications of different ML algorithms in different phases of the ASCs. The study highlights how ASCs can benefit from ML techniques and lead to ASC sustainability. Based on the study findings an ML applications framework for sustainable ASC is proposed. The framework identifies the role of ML algorithms in providing real-time analytic insights for pro-active data-driven decision-making in the ASCs and provides the researchers, practitioners, and policymakers with guidelines on the successful management of ASCs for improved agricultural productivity and sustainability
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