=Paper=
{{Paper
|id=Vol-2030/HAICTA_2017_paper13
|storemode=property
|title=SOFTROL Advisory System for Agri-Energy Complexes
|pdfUrl=https://ceur-ws.org/Vol-2030/HAICTA_2017_paper13.pdf
|volume=Vol-2030
|authors=Paweł Pietkiewicz,Krzysztof Nalepa,Wojciech Miąskowski,Maciej Neugebauer,Piotr Sołowiej
|dblpUrl=https://dblp.org/rec/conf/haicta/PietkiewiczNMNS17
}}
==SOFTROL Advisory System for Agri-Energy Complexes==
https://ceur-ws.org/Vol-2030/HAICTA_2017_paper13.pdf
SOFTROL Advisory System for Agri-Energy Complexes
Paweł Pietkiewicz1, Krzysztof Nalepa2, Wojciech Miąskowski3,
Maciej Neugebauer4, Piotr Sołowiej5
1
Faculty of Technical Sciences, University of Warmia and Mazury in Olsztyn, Poland, e-mail:
papiet@uwm.edu.pl
2
Faculty of Technical Sciences, University of Warmia and Mazury in Olsztyn, Poland, e-mail:
nalepka@uwm.edu.pl
3
Faculty of Technical Sciences, University of Warmia and Mazury in Olsztyn, Poland, e-mail:
wojmek@uwm.edu.pl
3
Faculty of Technical Sciences, University of Warmia and Mazury in Olsztyn, Poland, e-mail:
mak@uwm.edu.pl
3
Faculty of Technical Sciences, University of Warmia and Mazury in Olsztyn, Poland, e-mail:
pit@uwm.edu.pl
Abstract. The article presents the general concept, key assumptions and a
description of the SOFTROL advisory system. The system has been
implemented online, and it is available to all potential users. It supports virtual
design of agri-energy complexes and optimization calculations for maximize
farms’ ability to harness their energy potential and meet their energy needs
based on renewable energy sources.
Keywords: advisory system, agri-energy complexes, database
1 Introduction
Every agricultural undertaking is a consumer and a producer of energy. Energy
resources that have not been used up by a farm can be utilized productively. The
energy generated by a farm can be harnessed to cover the farm’s needs, and surplus
energy can be used to generate additional income and improve the farm’s economic
performance. This strategy underlies the operations of agri-energy complexes. An
agri-energy complex can be defined as an organization that utilizes all available
resources to meet its energy demand or to generate surplus energy.
Regardless of its size, production profile and organizational model, every farm has
specific energy needs and energy potential that can be utilized productively.
Therefore, even the smallest farm fits the definition of an agri-energy complex.
This study describes an advisory system for decision-making support in an agri-
energy complex with a specific production profile. The system has been designed and
built as part of key project No. POIG.01.01.02-00-016/08 entitled “Model agri-
energy complexes for distributed cogeneration units based on local dispersed
sources of energy”. The system can be implemented in the existing, currently
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developed and planned agricultural undertakings. In this study, the described system
is referred to as a potentially designed agri-energy complex (PDAC).
2 An agri-energy complex as a producer and consumer of energy
The energy potential of an agricultural undertaking has to be estimated to guarantee
the effectiveness of every polyoptimization system. The required infrastructure, type
and volume of wastes and by-products, surplus production and agricultural acreage
that can be dedicated to energy crops have to be described in detail.
A diagram of an agri-energy complex as a producer and consumer of energy is
presented in Figure 1.
Fig. 1. PDAC as a consumer and producer of energy (Pietkiewicz et al. 2014).
An agri-energy complex consumes three types of energy:
• electrical energy (lighting, household equipment, production equipment,
machines and electrical devices),
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• heat (indoor heating, water heating, meal preparation, drying agricultural
produce),
• mechanical energy (powering windmill pumps, conveyors, etc.).
In addition to conventional sources of energy (fossil fuels), an agri-energy
complex can also rely on renewable sources of energy to cover its energy needs:
• solar energy (liquid and hot air solar collectors, photovoltaic modules, passive
solar heating systems),
• wind energy (wind turbines for generating electricity and powering
compressors and pumps),
• hydraulic energy (low-power hydroelectric turbines),
• biomass (direct combustion and pyrolysis of plant wastes, straw, wood,
sawdust, energy crop residues; ethanol fermentation: cereal grain, wood, plant
wastes; methane fermentation: farm manure, plant wastes),
• liquid fuels (for powering combustion engines in transport vehicles and
machines, cogeneration units and fuel cells), fossil fuels.
The use of diverse energy sources and different methods for generating,
converting and storing energy supports the development of modular solutions that
rely on various combinations of the available technologies (Fig. 2).
Fig. 2. Modular solution for converting, storing and conditioning energy from renewable
sources (Nalepa et al. 2014)
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3 Computer software advisory system for planning the operations
of an agri-energy complex
Advisory systems consist of computer software that supports decision-making in
various areas of activity. Dendral was the first artificial intelligence project, which
led to the development of a computer software expert system in 1965. Dendral was
originally designed for chemists, but many expert systems for medical diagnosis
were derived from Dendral in successive decades. Medicine is the ideal field for an
advisory system, which relies on rules of inference based on information about
specific diseases that is gathered in databases. The system analyzes data input into
the program by a user who requires expert advice (advisory system). Advisory
systems are shells that are filled with knowledge by a user based on a set of rules.
There are limitless possibilities for developing advisory systems based on unique
data in a given area of interest.
Fig. 3. Diagram of client-server architecture (Pietkiewicz et al. 2014)
Key assumptions in the process of developing the SOFTROL advisory system
The discussed advisory system was developed based on the following key
assumptions:
• the designed system will have client-server architecture, and it will be
accessed via a web browser (Fig. 3);
• the user is not expected to possess expert knowledge about crop production,
materials or energy equipment,
• the system will be available to several groups of users with different levels
of access. This hierarchy will be established to distinguish between users
who have expert knowledge and users who require expert knowledge
(results output by the system),
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• inference rules have to guarantee that the system is flexible and suitable for
a wide range of applications. The authors’ role should be limited to
designing the system framework.
Main system functionalities
The SOFTROL system will have the following main functionalities based on the key
assumptions of the research project and the modular database concept:
• the system will collect data and provide users with information relating to
optimal technologies for the production of crops, animals, energy sources
and energy,
• the system will provide registered users with information relating to energy
generation devices and optimal technologies for the generation of heat and
electricity,
• the system will collect information about devices for the generation of heat
and energy from renewable sources and will use the resulting data in the
polyoptimization process,
• the system will assist users in selecting energy conversion devices based on
multiple criteria.
System users and their role in the system
Several groups of users were defined in line with the expert system concept and the
key assumptions for developing an advisory system for agri-energy complexes. A
hierarchy of users with different access levels was created to distinguish between
users who are providers of exert knowledge and users who request information from
the SOFTROL system. The following groups of users were defined:
• farmers, independent producers, associations of producers and local
governments who analyze and design agri-energy complexes, heat and
electricity producers who use or plan to use the technologies described in
the system,
• advisory organizations operating in the field of agriculture, power
generation, regional and national energy development,
• equipment manufacturers and distributors,
• producers and distributors of energy resources and fuels,
• scientific organizations that require access to the database for research
purposes.
Information collected in a relational database
The normalize the database, the process of collecting information will be governed
by the following rules:
• the system will collect personal information about users in the database,
including user identity, geographical location and administrative region.
Users who input information into the system on behalf of other users will be
placed in specific functional groups,
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• information relating to a user’s energy requirements will describe all
elements of the infrastructure and devices that cater to the user’s household
needs (farms), operational needs (institutions), demand for energy that is
sold (contracted) and generated from the existing resources,
• energy demand associated with the production and conversion of energy
resources will be indicated in the description of the proposed technology,
and it will not account for the user’s energy needs,
• a user’s energy potential is defined as natural resources and limitations. In
crop production, a user’s potential is determined by agricultural acreage; in
animal production – by the number of buildings and facilities for animal
rearing (based on the number of animals per unit area) which are owned or
have been contracted for energy generation purposes. In the above
approach, a user’s energy potential is evaluated based on the existing output
as well as potential outputs in the future. A user’s energy potential is
determined based on the area of farmland in different soil quality classes
and the area of production facilities.
In the process of developing the database, the information that can be directly
input into the system and the data associated with other types of information in the
user’s possession was identified in the modeled agri-energy complex. An analysis of
the modeled agri-energy complex revealed that most elements of the model are
indirectly linked with the norms, standards and average values applicable to
agricultural production. An individual farmer is not expected to be familiar with all
parameters relating to his farm or the production process. As regards large agri-
energy complexes and prospective farmers who are in the process of planning the
production of energy crops and/or energy generation, the values of most modeled
elements will be the expected system outputs.
The information stored in the database should be available and comprehensible for
an average user. Some of that information constitutes single-use data embedded in
the PDAC. Data entries can also relate to specific attributes describing various users
(potential crop production), which requires the creation of several subsets of
multiple-use data.
Modules of the SOFTROL advisory system
The database of the SOFTROL advisory system consists of several modules that are
closely linked with the user interface. The entities in the database have been defined.
The network connecting three database modules and their links with different entities
in the database are presented in Figure 4. The presented diagram was used in the
database normalization process.
In the user interface, the species and materials and carriers entities are separate
structures that are not allocated to any module. The devices entity overlaps the data
acquisition module on energy conversion devices. In view of the required quantity
and quality of data relating to energy generation devices and equipment used in the
production of energy resources, the database system has to be provided with
extensive mechanisms that control data access and the quality of input data. The
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information about producers, equipment efficiency and fuel is an integral part of the
module marked in green, but it is not an interface between the remaining database
modules.
Fig. 4. Diagram of connections between database modules in the SOFTROL advisory system
(Pietkiewicz et al. 2014).
4 Development of the SOFTROL advisory system
The database and the user interface were designed based on the described concept.
Information was stored in a MySQL database, and the user interface was written in
PHP, a popular object-oriented language for developing web applications. PHP is
used mainly for server-side scripting (Hilton et al. 1999)
Data acquisition model on the technology of converting and storing energy
resources
The designed system contains a data acquisition model on the technology of
converting and storing energy resources. We will use this module to describe various
stages in the process of designing and implementing the advisory system.
In a software system, technology is defined as a chain of operations, which
represent successive stages in the process of achieving a specific goal, such as the
production of fuel or an energy resource. In a farm, different technologies are used to
produce crops and animals, but similar stages can be identified in every production
technology. The designed system should contain a universal technology for designing
any process in an agri-energy complex. The universal technology should also support
the design of other technologies that are not directly linked with crop or animal
production, such as biogas production technology for biogas plants in sewage
treatment facilities. The universal technology was developed in a series of steps:
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• significant similarities between the modeled crop and animal production
technologies were identified,
• significant differences between the modeled crop and animal production
technologies were identified
• parameters for minimizing differences between the modeled crop and
animal production technologies were determined,
• data sets and parameters were expanded to account for the needs of other
technologies.
Table 1. A comparison of the main stages in typical crop, animal and universal production
technologies (Pietkiewicz et al. 2014)
Crop production Animal Universal
production production
technology
Selection of species Selection of species Selection of species
and variety and breed
Land cultivation Preparation of Preparation of
animal facilities production area
Production or Selection of Beginning of
purchase of seeds, breeding stock, breeding
pre-sowing preparation for
fertilization, sowing mating, mating /
and planting purchase of animals
Cultivation: Nutrition Nutrition
fertilization
Cultivation: crop Disease prevention Pest and disease
protection and treatment control
Harvest Slaughter or sale Harvest
Similarities between crop and animal production systems were identified to
standardize their description in the advisory system. The simplest technological
model is composed of a series of successive stages, where selected stages are
obligatory and other stages are optional. Different stages in crop and animal
production technologies were compared. The results of the comparison are presented
in Table 1. Similar stages were identified in all of the compared technologies.
Despite significant differences in treatments, the conditions under which they are
applied and the time of their application, every stage of the production process
involves a specific number of operations. A given set of materials, devices and
workers necessary for the described operations can be allocated to every stage of the
production process. This approach has been adopted to evaluate the energy
consumption, cost and duration of every operation. In each technology, the end
product can serve as a resource in another technology or as an energy carrier for the
generation of electricity, heat or mechanical energy. End products can also be sold.
The correlations between data sets relating to technologies for the generation of
energy or the production of energy resources are presented in a diagram in Figure 5.
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A specific technology is used to produce one or more products, including energy
resources. The entire technological process is a chain of many technological
operations. Specific materials, resources and devices can be allocated to each
operation. Complex technologies that can be developed based on the processes listed
in the database are also indicated in the diagram.
Fig. 5. Correlations between data sets describing individual components of technologies for
the generation of energy and production of energy resources (Pietkiewicz et al. 2011)
Several correlated tables were developed to design the database of production
technologies. The structure and correlations between the tables are presented in
Figure 6.
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Fig. 6. Structure of the database relating to the data acquisition module on the energy potential
and the energy needs of the PDAC (Pietkiewicz et al. 2014)
A similar approach was used to develop the remaining database modules. The
design and implementation of database modules in the advisory system was
described in a previous study (“SOFTROL – Energy directly from nature”,
Pietkiewicz et al. 2014).
Simulation module
The main element of the advisory system is a simulation module which supports the
development of other technological systems based on the adopted parameters of
energy potential, energy demand, energy consumption during fuel production, and
energy conversion to electricity and heat. Due to copyright and trade secrets
regarding the system as a product for implementation, detailed algorithms have not
been described.
The user interface enables a user to design an agri-energy complex based on
information relating to a farm’s geographical location, natural resources (acreage,
soil class), the existing or planned buildings. Based on the information collected in
database modules, the system develops a subset of technologies that can be applied in
the designed complex. Selected technologies, including those that meet boundary
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conditions, can be eliminated by the SOFTROL system based on PDAC preferences
(Pietkiewicz et al. 2012).
Selecting a simulation module is a collection of PPKA energy efficiency variants.
By default, the system is presented in the order of the most cost-effective. The user
has the possibility to change the criteria, such as the criterion of covering the PPKA
energy demand.
The described system is an advisory system. The user decides on the optimization
criterion alone, and in no way should it coincide with the criteria established in the
system.
Fig. 7. Subset of technologies that meet boundary conditions in the defined PDAC
(Pietkiewicz et al. 2014)
A list of technologies that meet boundary conditions in the defined PDAC and
can be eliminated from further analyses is presented in Figure 7. Logical fields in
Yes/No format for eliminating selected technologies are shown on the left. When the
list of technologies defined in the PDAC is closed, the user can begin to design
variant solutions for harnessing the energy potential of the PDAC. This operation
lasts from several seconds to several dozen minutes, depending on the number of
variables in the energy potential of the PDAC and the size of data sets containing
technologies that meet boundary conditions and can be used in further analyses.
The calculated results are presented by the system (Fig. 7). The user can access
detailed information about the variant solution for harnessing the energy potential of
the PDAC by clicking on the Variant details link.
5 Conclusions
The SOFTROL system has been designed for a wide range of applications. The
system contains dedicated algorithms, and it is a complete product that is ready for
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commercial implementation. According to the authors, the system can be used for the
following purposes:
• to provide access to information about energy equipment available on the
market,
• to update the database of devices based on the data supplied by the
manufacturers of energy equipment,
• to select the optimal energy generation system based the system’s ability to
rapidly process the variables input by the user,
• to support decision making in the production of energy crops,
• to collect and analyze information about the use of renewable energy
sources in various regions of the country,
• to popularize information about renewable energy sources,
• to lower the costs of systems that harness the energy potential of farms,
• to maximize farms’ ability to harness their energy potential and meet their
energy needs based on renewable energy sources.
The discussed advisory system has been implemented for educational purposes at the
Faculty of Technical Sciences of the University of Warmia and Mazury in Olsztyn,
Poland. The system has been designed and built as part of key project No.
POIG.01.01.02-00-016/08 entitled “Model agro-energy complexes as an example of
distributed cogeneration based on local renewable energy sources”, and it is ready
for commercial implementation.
References
1. Hilton C., Willis J., (1999) – Building Database Applications on the Web Using
PHP3. Addison-Wesley Pub
2. Pietkiewicz P., Nalepa K., Miąskowski W. (2014) SOFTROL – Energy directly
from nature. Multilayered computer model of agroenergetic complex in relational
database technology (in polish). Gdańsk
3. Pietkiewicz P, Nalepa K., Miąskowski W. (2011) – Composition of energy
balance algorithms in the advisory system for energetic investments based on
agriculture production(in polish). Mechanik vol.7/2011, Warszawa. Agenda
Wydawnicza SIMP
4. Pietkiewicz P, Nalepa K., Miąskowski W. (2012) – Initial conditions test
realization for the selection of energy production technology and energetic raw
materials in the advisory system (in polish). Mechanik vol.7/2012, Warszawa.
Agenda Wydawnicza SIMP
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