Nutrient recovery from digestates : techniques and end-products

In nitrate vulnerable zones application of animal manure to land is limited. Digestate from anaerobic digestion plants competes with manure for nutrient disposal on arable land, which forms a serious hinder for the biogas sector to develop in these regions. Hence, one of its biggest challenges is to find cost-effective and sustainable ways for digestate processing or disposal. Furthermore, primary phosphorus resources are becoming scarce and expensive and will be depleted within a certain time. This urges the need to recycle P from secondary sources, like digestate or manure. From a sustainability point of view, it seems therefore no more than logical that digestate processing techniques switched their focus to nutrient recovery rather than nutrient removal. This paper gives an overview of digestate processing techniques, with a special focus on nutrient recovery techniques. In this paper nutrient recovery techniques are delineated as techniques that (1) create an end-product with higher nutrient concentrations than the raw digestate or (2) separate the envisaged nutrients from organic compounds that are undesirable in the end-product, with the aim to produce an end-product that is fit for use in chemical or fertiliser industry or as a mineral fertiliser replacement. Various nutrient recovery techniques are described, with attention for some technical bottlenecks and the current state of development. Where possible, physicochemical characteristics of the endproducts are given

Enterprise Systems Analysis and Modelling

In ES implementations, process modelling is a critical and often overlooked activity. This paper proposes a framework for process modelling of ES. The four steps method involves: Current Situation Analysis, Business Process Improvements and Requirements, Gap Analysis, and To-be process to develop. Outputs of the methodology are an interdependent set of organizational and system proposed changes, and feedback loops to the ES vendors and to the strategy of the firm. In-depth case studies and extensive literature review provides methodological support. For practitioners, this study provides useful insights into one of the reasons by which companies could be frustrated with ES implementation.E-business, ERP

An economic order quantity stochastic dynamic optimization model in a logistic 4.0 environment

This paper proposes a stock dynamic sizing optimization under the Logistic 4.0 environment. The safety stock is conceived to fill up the demand variability, providing continuous stock availability. Logistic 4.0 and the smart factory topics are considered. It focuses on vertical integration to implement flexible and reconfigurable smart production systems using the information system integration in order to optimize material flow in a 4.0 full-service approach. The proposed methodology aims to reduce the occurring stock-out events through a link among the wear-out items rate and the downstream logistic demand. The failure rate items trend is obtained through life-cycle state detection by a curve fitting technique. Therefore, the optimal safety stock size is calculated and then validated by an auto-tuning iterative modified algorithm. In this study, the reorder time has been optimized. The case study refers to the material management of a very high-speed train

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS